Ivor Wai-Hung TSANG Assistant Professor School of Computer Engineering Nanyang Technological University Singapore 639798 Email: IvorTsang (at) ntu (dot) edu (dot) sg Ivor (dot) Tsang (at) gmail (dot) com

The LATEST software of Feature Generating Machine for ultrahigh dimensional feature selection, which is 1000 times faster than the old version, is now available at here, and some lecture notes are available at Machine Learning Summer School 2011

The tutorial notes for Domain Adaptation in Real-world Applications Part I and Part II at ACML 2012 are available

The tutorial notes for Domain Transfer Learning at IEEE CVPR 2012 are available here

Dr Ivor Wai-Hung Tsang is an Assistant Professor in the School of Computer Engineering of Nanyang Technological University. He is also the Deputy Director of the Centre for Computational Intelligence. He received his Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology (HKUST) in 2007. His research expertise lies in support vector machines, transfer learning, scalable machine learning for big data with millions of dimensions, and their applications to data mining and pattern recognitions. He has 100 research papers published in refereed international journals and conference proceedings, including Journal of Machine Learning Research (JMLR), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Neural Computation, NIPS, ICML, UAI, AISTATS, SIGKDD, IJCAI, AAAI, ICCV, CVPR, ICDM, etc. He has given tutorials regarding domain selection and adaptation at CVPR 2012 and ACML 2012 . He also delivered invited lectures and talks on large-scale machine learning and extremely high dimensional feature selection at Machine Learning Summer School (MLSS), and many world-class universities and research institutes.

In 2009 Dr Tsang was conferred the 2008 Natural Science Award (Class II) by Ministry of Education, China, which recognized his contributions to kernel methods. Besides this, he has received the prestigious IEEE Transactions on Neural Networks Outstanding 2004 Paper Award in 2006, and a number of best paper awards and honors from reputable international conferences, including the Best Student Paper Award at the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010), the Best Paper Award at the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2011), the Best Poster Award Honorable Mention at the Asian Conference on Machine Learning (ACML 2012), the Best Student Paper Nomination at the IEEE Congress on Evolutionary Computation (CEC 2012), and the Best Paper Award from the IEEE Hong Kong Chapter of Signal Processing Postgraduate Forum in 2006. He was also awarded the Microsoft Fellowship 2005, and the 12th European Conference on Computer Vision (ECCV 2012) Outstanding Reviewer Award.

"Theory without Practice is empty; but Practice without Theory is blind" - Immanuel Kant

Machine learning and Data mining:

Feature selection, Sparse representation and Sparse coding
Domain selection, Domain adaptation, Dataset shift, Transfer learning, Multitask learning and Distribution matching
Core vector machines, Coreset approximation, Large-scale optimization and Scalable machine learning for big data
Learning from ambiguity, Multiple instance learning , Clustering, Unsupervised and Semi-supervised learning
Many class prediction, Multi-label learning, Ordinal regression and Structured Prediction
Kernel learning, Output-Kernel learning and Kernel Generation
Performance metric evaluation and optimization

Applications:

Internet vision, Web-Scale Multimedia retrieval and annotation
Computer vision, Scene and Object recognition
Text mining, Sentiment Analysis, Coreference Resolution and Information extraction

NTU-PI, A*STAR TSRP – "Large Scale Hybrid Storage System"
Co-PI, A*STAR TSRP – "User and Domain-Driven Data Analytics as a Service Framework (UDDDASF)"
Co-PI, Rolls Royce Project – "Large-Scale Data Management"
NTU-PI, DSO – "Towards Automatic Template Extraction with Minimal Human Supervision"
PI, NTU and A*STAR IHPC Joint Project – "Large Scale Domain Adaptation Machines: Information Integration, Revolution and Transfer"
Co-PI, NRF IDM – "Next Generation Annotation Techniques for Consumer Photos and Videos"
PI, MOE AcRF Tier 1 – "Transferable Kernel Machines: Knowledge Integration, Extraction and Transfer"
PI, NTU Startup Grant

Qi Mao (PhD student)
Mingkui Tan (PhD student)
Joey Tianyi Zhou (PhD student)
Qiaoliang Xiang (Master student)

Lin Chen (PhD student, with Prof. Dong Xu)
Wen Li (PhD student, with Prof. Dong Xu)
Xinxing Xu (PhD student, with Prof. Dong Xu)
Yiteng Zhai (PhD student, with Prof. Yew Soon Ong)

Minh Luan Nguyen
Shaohua Yang

Dr. Chun-Wei Seah (PhD student, with Prof. Yew Soon Ong, now working at DSO National Laboratories)
Dr. Shenghua Gao (PhD student, with Prof. Liang-Tien Chia, now Researcher at ADSC)
Dr. Jianbo Yang (Postdoc, now Postdoc in Department of electrical and computer engineering of Duke University)
Dr. Lixin Duan (PhD student, with Prof. Dong Xu, now Researcher at SAP Research Singapore)
Dr. Jinfeng Zhuang (PhD student, with Prof. Steven Hoi, now working at Microsoft)
Li Wang (Research Assistant, now PhD student in Mathematics Department of University of California, San Diego)
Xinming Zhang (Research Assistant)

Qi Mao, Ivor W. Tsang. A Feature Selection Method for Multivariate Performance Measures. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. (PDF)(A preliminary version appeared in arXiv)
How to optimize multivariate performance measures for Multiple-Instance Learning via Embedded instance Selection (MILES) ?

Qi Mao, Ivor W. Tsang. Efficient Multi-Template Learning for Structured Prediction. IEEE Transactions on Neural Networks and Learning Systems, 24(2): 248 - 261, Feb 2013. (PDF) (A preliminary version appeared in arXiv)

Yiteng Zhai, Mingkui Tan, Ivor W. Tsang, Yew-Soon Ong. Discovering Support and Affiliated Features from Very High Dimensions. Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, 2012. (PDF) [Long Oral]
How to automatically discover groups of informative features from very high dimensions ?
How to efficiently handle O(m^2) correlation constraints among features within 1 minute when the dimensionality m is 8 Million ?

Qi Mao, Ivor W. Tsang. Optimizing Performance Measures for Feature Selection. Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM 2011), Vancouver, Canada, Dec 2011. (PDF)
A two-layer cutting plane algorithm together with a primal MKL algorithm are proposed for optimizing performance measures for feature selection where the problem has exponential size of both feature groups and label configurations for a given dataset

Mingkui Tan, Li Wang, Ivor W. Tsang. Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, June 2010. (PDF) (Software)
A linear-time SVM-type feature selection algorithm is proposed for large-scale and extremely high dimensional datasets
A very small subset of non-monotonic features can be identified from 3 Million features for suspicious URLs prediction

Mingkui Tan, Ivor W. Tsang, Li Wang. Is Matching Pursuit Solving Convex Problems? arXiv :1302.5010, Feb 2013. (link)
A General Matching Pursuit (GMP) is proposed for very large-scale sparse recovery problems (e.g. 100k dictionary entries)
It can recover any k-sparse signals if the restricted isometry constant sigma_k < 0.307-nu, where nu can be arbitrarily close to 0
When dealing with a batch of signals, the computational burden can be much reduced using a batch-mode matching pursuit, which can be up to 500 times faster than L1-methods
Its decoding speed is much faster than many recently developed methods including PGH, OMPRA, FISTA, ADM, SP, CoSaMP, AIHT, OMP, Shotgun (Parallel)
The decoding speed of GMP on face recognition is even as fast as L2-methods but it can achieve more robust and better recognition performances than L2-methods

Shenghua Gao, Ivor W. Tsang, Liang-Tien Chia. Sparse Representation with Kernels. IEEE Transactions on Image Processing , 22(2):423 - 434, Feb 2013. (PDF)
More results on Object Recognition, Face Recognition and Kernel Matrix Approximation for Classification

Shenghua Gao, Ivor W. Tsang, Liang-Tien Chia. Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35:(1):92-104, Jan 2013. (PDF)
Our Laplacian Sparse Coding achieves 89.78% accuracy on Scene15 dataset and 85.27% accuracy on UIUC-Sport dataset

Mingkui Tan, Ivor W. Tsang, Li Wang, Xinming Zhang. Convex Matching Pursuit for Large-scale Sparse Coding and Subset Selections. Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), Toronto, Canada, 2012. (PDF)
A robust and convex matching pursuit is proposed for very large-scale sparse approximation problems
Both the objective value and the objective difference are strictly monotonically decreasing
Its decoding speed is much faster than many recently developed methods including FISTA, ADM, SP, CoSaMP, AIHT, OMP

Shenghua Gao, Liang-Tien Chia, Ivor W. Tsang. Multi-layer Group Sparse Coding - for Concurrent Image Classification and Annotation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado Springs, Colorado, June 2011. (PDF)
How can image classification and image annotation help each other ?

Shenghua Gao, Ivor W. Tsang, Liang-Tien Chia. Kernel Sparse Representation for Image Classification and Face Recognition. Proceedings of the 11th European Conference on Computer Vision (ECCV 2010), Crete, Greece, September 2010. (PDF)

Shenghua Gao, Ivor W. Tsang, Liang-Tien Chia, Peilin Zhao. Local Features Are Not Lonely - Laplacian Sparse Coding for Image Classification. Proceedings of the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco, CA, June 2010. (PDF)
The local dependency among sparse codes is introduced to preserve local consistence of sparse representation

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong, Qi Mao. Learning Target Predictive Function without Target Labels. Proceedings of the IEEE International Conference on Data Mining (ICDM 2012), Dec 2012. (PDF)
How to address the two major issues in domain adaptation, namely Variance (the change in data distribution) and Source hypothesis bias ?
How to choose relevant source domains to the target domain where labeled data are absent ?

Chun-Wei Seah, Yew-Soon Ong, Ivor W. Tsang. Combating Negative Transfer from Predictive Distribution Differences. IEEE Transactions on Cybernetics, 2013. (PDF)

Jian Bo Yang, Qi Mao, Qiao Liang Xiang, Ivor W. Tsang, Kian Ming Adam Chai and Hai Leong Chieu. Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach. Proceedings of the Conference on Empirical Methods in Natural Language Processing and Conference on Natural Language Learning (EMNLP-CoNLL), Jeju, South Korea, July 2012. (PDF)
A domain adaptation algorithm is introduced for coreference resolution (supervised clustering) in resource poor domains
Moreover, we present a general framework to optimize for any user-specified performance measure

Liang Feng, Yew-Soon Ong, Ivor W. Tsang, Ah-Hwee Tan. An Evolutionary Search Paradigm that Learns with Past Experiences. Proceedings of the IEEE World Congress on Computational Intelligence, Congress on Evolutionary Computation, June 2012. (PDF)
#This paper was nominated for the Best Student Paper at the IEEE Congress on Evolutionary Computation 2012
How to transfer knowledge for clustering and solving NP hard optimization problems ?

Lixin Duan, Dong Xu, Ivor W. Tsang. Domain Adaptation from Multiple Sources: A Domain-Dependent Regularization Approach. IEEE Transactions on Neural Networks and Learning Systems, 23(3):504 - 518, March 2012. (PDF)

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong. Healing Sample Selection Bias by Source Classifier Selection. Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM 2011), pp. 577 - 586, Vancouver, Canada, Dec 2011. (PDF) [Full Paper, Acceptance rate = 13%]
How to handle dataset bias - the change of class distribution in training source data and test target data ?
How to alleviate Negative Transfer ?
How to efficiently handle large-scale transduction ?
How to select and combine source classifiers for the target domain ?

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong, Kee-Khoon Lee. Predictive Distribution Matching SVM for Multi-Domain Learning. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), Barcelona, Spain, September 2010. (PDF)
We proposed Predictive Distribution Matching SVM (PDM-SVM), which iteratively constructs a graph for identifying the patterns of positive transfer and negative transfer from multiple sources, to address Negative Transfer in Domain Adaptation

Lixin Duan, Ivor W. Tsang, Dong Xu, Tat-Seng Chua. Domain Adaptation from Multiple Sources via Auxiliary Classifiers. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Quebec, June 2009. (PDF)
How to use target unlabeled data and auxiliary classifiers from multiple sources to learn the target classifier for domain adaptation ?

Bo Chen, Wai Lam, Ivor W. Tsang, Tak-Lam Wong. Discovering Low-Rank Shared Concept Space for Adapting Text Mining Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6):1284 - 1297, June 2013. (PDF)

Lixin Duan, Dong Xu, Ivor W. Tsang. Learning with Augmented Features for Heterogeneous Domain Adaptation. Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, June 2012. (PDF)

Lixin Duan, Dong Xu, Ivor W. Tsang, Jiebo Luo. Visual Event Recognition in Videos by Learning from Web Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9):1667 - 1680, Sept 2012. (PDF)

Lixin Duan, Ivor W. Tsang, Dong Xu. Domain Transfer Multiple Kernel Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):465-479, March 2012. (PDF)

Yiming Liu, Dong Xu, Ivor W. Tsang, Jiebo Luo. Textual Query of Personal Photos Facilitated by Large-scale Web Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5): 1022 - 1036, May 2011. (PDF)
Automatic Web Image Retrieval is introduced to collect loosely labeled web images for a textual query
Then a cross-domain relevant feedback using loosely labeled web images is proposed to retrieve personal photos

Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok and Qiang Yang. Domain Adaptation via Transfer Component Analysis. IEEE Transactions on Neural Networks, 22(2): 199 - 210, Feb 2011. (PDF)

Lixin Duan, Dong Xu, Ivor W. Tsang, Jiebo Luo. Visual Event Recognition in Videos by Learning from Web Data. Proceedings of the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco, CA, June 2010. (PDF) [Oral, Acceptance rate = 4.5%]
#This paper was awarded with the best student paper prize at the 23rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010)

Brian Mak, Tsz-Chung Lai, Ivor W. Tsang, James T. Kwok. Maximum Penalized Likelihood Kernel Regression for Fast Adaptation. IEEE Transactions on Audio, Speech and Language Processing, 17(7): 1372-1381, September 2009. (PDF)
Maximum Penalized Likelihood Kernel Regression is proposed to adapt a new HMM from the structure of existing Hidden Markov Models (HMMs) for a new speaker with very few speech data

Bo Chen, Wai Lam, Ivor W. Tsang, Tak-Lam Wong. Extracting Discriminative Concepts for Domain Adaptation in Text Mining. Proceedings of the 15th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2009), Paris, June 2009. (PDF)
Discriminative Subspace Extraction and Feature Propagation are proposed for cross-domain text mining

Lixin Duan, Ivor W. Tsang, Dong Xu, Stephen J. Maybank. Domain Transfer SVM for Video Concept Detection. Proceedings of the 22nd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami Beach, Florida, USA, June 2009.(PDF)
Domain Transfer SVM combines both Multiple Kernel Learning (MKL) and Distribution Matching via MMD for Semi-Supervised Learning/Transduction

Ivor W. Tsang, James T. Kwok, Brian Mak, Kai Zhang, Jeffrey J. Pan. Fast speaker adaption via maximum penalized likelihood kernel regression. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06), Toulouse, France, May 2006. (PDF)
#This paper was awarded with the Best Paper Award from the IEEE Hong Kong Chapter of Signal Processing Postgraduate Forum 2006

Nan Li, Ivor W. Tsang, Zhi-Hua Zhou. Efficient Optimization of Performance Measures by Classifier Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6): 1370-1382, June 2013. (PDF)
How to efficiently learn a nonlinear classifier for optimizing non-convex and non-smooth performance measures ?

Qi Mao, Ivor W. Tsang. Efficient Multi-Template Learning for Structured Prediction. IEEE Transactions on Neural Networks and Learning Systems, 24(2): 248 - 261, Feb 2013. (PDF) (A preliminary version appeared in arXiv)

Yiteng Zhai, Mingkui Tan, Ivor W. Tsang, Yew-Soon Ong. Discovering Support and Affiliated Features from Very High Dimensions. Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, 2012. (PDF) [Long Oral]
How to automatically discover groups of informative features from very high dimensions ?
How to efficiently handle O(m^2) correlation constraints among features within 1 minute when the dimensionality m is 8 Million ?

Mingkui Tan, Ivor W. Tsang, Li Wang, Xinming Zhang. Convex Matching Pursuit for Large-scale Sparse Coding and Subset Selections. Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI), Toronto, Canada, 2012. (PDF)
A robust and convex matching pursuit is proposed for very large-scale sparse approximation problems
Both the objective value and the objective difference are strictly monotonically decreasing
Its decoding speed is much faster than many recently developed methods including FISTA, ADM, SP, CoSaMP, AIHT, OMP

Lin Chen, Ivor W. Tsang, Dong Xu. Laplacian Embedded Regression for Scalable Manifold Regularization. IEEE Transactions on Neural Networks and Learning Systems, 23(6):902 - 915, June 2012. (PDF)(Code)
How to solve large-scale Manifold Regularization ?

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong. Healing Sample Selection Bias by Source Classifier Selection. Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM 2011), pp. 577 - 586, Vancouver, Canada, Dec 2011. (PDF) [Full Paper, Acceptance rate = 13%]
How to handle dataset bias - the change of class distribution in training source data and test target data ?
How to alleviate Negative Transfer?
How to efficiently handle large-scale transduction ?
How to select and combine source classifiers for the target domain ?

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. A Family of Simple Non-Parametric Kernel Learning Algorithms. Journal of Machine Learning Research(JMLR), 12:1313-1347, 2011.(PDF)
A scalable algorithm is proposed to solve a family of large-scale SDP problems, including Colored Maximum Variance Unfolding, Minimum Volume Embedding, Structure Preserving Embedding and other non-parametric kernel learning problems

Mingkui Tan, Li Wang, Ivor W. Tsang. Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. Proceedings of the 27th International Conference on Machine Learning (ICML 2010), Haifa, Israel, June 2010. (PDF) (Software)
A linear-time SVM-type feature selection algorithm is proposed for large-scale and extremely high dimensional datasets
A very small subset of non-monotonic features can be identified from 3 Million features for suspicious URLs prediction

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. SimpleNPKL: Simple Non-Parametric Kernel Learning. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Quebec, June 2009. (PDF)
By exploiting the sparse structure, the Large-Scale Semi-Definite Programming (SDP) problem of Non-Parametric Kernel Learning with Pairwise Constraints can be solved by a very simple and scalable convex algorithm
A 5000-by-5000 non-parametric kernel matrix (25M variables) can be learned within a minute

Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou. Tighter and Convex Maximum Margin Clustering. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, Journal of Machine Learning Research W & CPs, Vol. 5, pp. 344-351, Clearwater Beach, Florida, USA, April 2009. (PDF) (Software) [Plenary Oral, Acceptance rate = 10%]
The mixed integer program in MMC can be solved by a scalable and efficient multiple output-kernel learning algorithm, which achieves a tighter approximation than the SDP relaxation

Kai Zhang, Ivor W. Tsang, James T. Kwok. Improved Nystrom low rank approximation and error analysis. Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML), pp.1232-1239, Helsinki, Finland. July 2008.(PDF) (Software)
How to find landmark points from a theoretical perspective to improve Nystrom Algorithm ?

Ivor W. Tsang, Andras Kocsor, James T. Kwok. Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines. IEEE Transactions on Neural Networks, 19(4): 610-624, April 2008. (PDF)

Ivor W. Tsang, Andras Kocsor, James T. Kwok. Simpler core vector machines with enclosing balls. Proceedings of the Twenty-Fourth International Conference on Machine Learning (ICML), pp.911-918, Corvallis, Oregon, USA, June 2007.(PDF) (Software)
SVM can be solved by the multi-scaled Enclosing Ball problem in Computational Geometry

Ivor W. Tsang, James T. Kwok. Large-scale sparsified manifold regularization. Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 1401-1408, Vancouver, Canada, December 2006. (PDF) [Plenary Oral, Acceptance rate = 3%] (A preliminary version appeared in NIPS 2005 Workshop on Large Scale Kernel Machines)
The manifold regularizer is transformed as pairwise constraints, which avoids matrix inversion

Ivor W. Tsang, James T. Kwok, Jacek M. Zurada. Generalized core vector machines. IEEE Transactions on Neural Networks, 17(5): 1126- 1140, Sept 2006. (PDF) (Software)

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung. Core vector machines: Fast SVM training on very large data sets. Journal of Machine Learning Research, 6:363-392, 2005. (PDF) (Software)

Ivor W. Tsang, James T. Kwok, Kimo T. Lai. Core Vector Regression for Very Large Regression Problems. Proceedings of the Twentieth-Second International Conference on Machine Learning (ICML-2005), pp.913-920, Bonn, Germany, August 2005.(PDF) (Software)

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung. Very large SVM training using core vector machines. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), Barbados, January 2005. (PDF)
SVM can be transformed equivalently as the Minimum Enclosing Ball problem in Computational Geometry
An approximation algorithm using coresets is firstly proposed in solving large-scale SVMs

Qi Mao, Ivor W. Tsang. A Feature Selection Method for Multivariate Performance Measures. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. (PDF)(A preliminary version appeared in arXiv)
How to optimize multivariate performance measures for Multiple-Instance Learning via Embedded instance Selection (MILES) ?

Xinxing Xu, Ivor W. Tsang, Dong Xu. Handling Ambiguity via Input-Output Kernel Learning. Proceedings of the IEEE International Conference on Data Mining (ICDM 2012), Dec 2012. [Full Paper, Acceptance rate = 11%]

Wen Li, Lixin Duan, Ivor W. Tsang, Dong Xu. Co-Labeling: A New Multi-view Learning Approach for Ambiguous Problems. Proceedings of the IEEE International Conference on Data Mining (ICDM 2012), Dec 2012. [Full Paper, Acceptance rate = 11%] (PDF)

Wen Li, Lixin Duan, Ivor W. Tsang, Dong Xu. Batch Mode Adaptive Multiple Instance Learning for Computer Vision Tasks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2012), Providence, Rhode Island, June 2012. (PDF)

Lixin Duan, Wen Li, Ivor W. Tsang, Dong Xu. Improving Web Image Search by Bag-based Re-ranking. IEEE Transactions on Image Processing, 20(11):3280--3290, November 2011. (PDF)
The notion of different level of ambiguity in positive bags and negative bags are introduced
How to automatically create noisy label for unlabeled web data ?
How to handle a large amount of label noise in web data ?

Wen Li, Lixin Duan, Dong Xu, Ivor W. Tsang. Text-based Image Retrieval using Progressive Multi-Instance Learning. Proceedings of the International Conference on Computer Vision (ICCV 2011), Barcelona, Spain, Nov 2011. (PDF)
How to select reliable positive bags progressively to improve the performance of MIL ?

Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou. A Convex Method for Locating Regions of Interest with Multi-Instance Learning. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009), pp.15-30, Bled, Slovenia, September 2009. (PDF) (Software)
Instead of bag prediction only, two convex and scalable Key-Instance SVMs via output-kernel learning are proposed to identify the Key Instance (KI) in positive bags and to predict bags simultaneously
Instance level KI-SVM can accurately locate the region of KI inside a positive bag; while Bag level KI-SVM achieves better bag prediction performance

Joey Tianyi Zhou, Sinno Jialin Pan, Qi Mao, Ivor W. Tsang. Multi-view Positive and Unlabeled Learning. Proceedings of the Asian Conference on Machine Learning (ACML), Journal of Machine Learning Research W & CPs, Nov 2012. (PDF) [Full Paper]
#This paper received the Best Poster Award Honorable Mention at the Asian Conference on Machine Learning (ACML 2012)

Lin Chen, Ivor W. Tsang, Dong Xu. Laplacian Embedded Regression for Scalable Manifold Regularization. IEEE Transactions on Neural Networks and Learning Systems, 23(6):902 - 915, June 2012. (PDF) (Code)
How to solve large-scale Manifold Regularization ?

Qi Mao, Ivor W. Tsang. Parameter-Free Spectral Kernel Learning. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), pp. 350-357, Catalina Island, California, July 2010. (PDF) [Plenary Oral, Acceptance rate = 11.5%]
For a given Laplacian matrix, a parameter-free Spectral Kernel Learning is proposed to learn an ideal kernel with the closed-form solution for transduction

Ivor W. Tsang, James T. Kwok. Large-scale sparsified manifold regularization. Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 1401-1408, Vancouver, Canada, December 2006. (PDF) [Plenary Oral, Acceptance rate = 3%] (A preliminary version appeared in NIPS 2005 Workshop on Large Scale Kernel Machines)
The manifold regularizer is transformed as pairwise constraints, which avoids matrix inversion

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong. Transductive Ordinal Regression. IEEE Transactions on Neural Networks and Learning Systems, 23(7):1074 - 1086, July 2012. (PDF)

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong. Healing Sample Selection Bias by Source Classifier Selection. Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM 2011), pp. 577 - 586, Vancouver, Canada, Dec 2011. (PDF) [Full Paper, Acceptance rate = 13%]
How to handle dataset bias - the change of class distribution in training source data and test target data ?
How to alleviate Negative Transfer?
How to efficiently handle large-scale transduction ?
How to select and combine source classifiers for the target domain ?

Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical. IEEE Transactions on Neural Networks, 20(4): 583-596, April 2009. (PDF) (Software)
CCCP for MMC/TSVM can be shown as alternating optimization
Why do local optimization methods for MMC/TSVM easily get stuck in local minima ?

Xinxing Xu, Ivor W. Tsang, Dong Xu. Handling Ambiguity via Input-Output Kernel Learning. Proceedings of the IEEE International Conference on Data Mining (ICDM 2012), Dec 2012. [Full Paper, Acceptance rate = 11%]

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. A Family of Simple Non-Parametric Kernel Learning Algorithms. Journal of Machine Learning Research(JMLR), 12:1313-1347, 2011.(PDF)
A scalable algorithm is proposed to solve a family of large-scale SDP problems, including Colored Maximum Variance Unfolding, Minimum Volume Embedding, Structure Preserving Embedding and other non-parametric kernel learning problems

Qi Mao, Ivor W. Tsang. Parameter-Free Spectral Kernel Learning. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), pp. 350-357, Catalina Island, California, July 2010. (PDF) [Plenary Oral, Acceptance rate = 11.5%]
For a given Laplacian matrix, a parameter-free Spectral Kernel Learning is proposed to learn an ideal kernel with the closed-form solution for transduction

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. SimpleNPKL: Simple Non-Parametric Kernel Learning. Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Montreal, Quebec, June 2009. (PDF)
By exploiting the sparse structure, the Large-Scale Semi-Definite Programming (SDP) problem of Non-Parametric Kernel Learning with Pairwise Constraints can be solved by a very simple and scalable convex algorithm
A 5000-by-5000 non-parametric kernel matrix (25M variables) can be learned within a minute

James T. Kwok, Ivor W. Tsang. Learning with idealized kernels. Proceedings of the International Conference on Machine Learning (ICML 2003), pp.400-407, Washington, D.C., USA, August 2003. (PDF)
Combination of BOTH Similar and Dissimilar Side information is firstly introduced as Distance constraints for learning a kernel in semi-supervised setting

Jian Bo Yang, Qi Mao, Qiao Liang Xiang, Ivor W. Tsang, Kian Ming Adam Chai and Hai Leong Chieu. Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach. Proceedings of the Conference on Empirical Methods in Natural Language Processing and Conference on Natural Language Learning (EMNLP-CoNLL), Jeju, South Korea, July 2012. (PDF)
A domain adaptation algorithm is introduced for coreference resolution (supervised clustering) in resource poor domains
Moreover, we present a general framework to optimize for any user-specified performance measure

Qiaoliang Xiang, Qi Mao, Kian Ming Chai, Hai Leong Chieu, Ivor W. Tsang, Zhendong Zhao. A Split-Merge Framework for Comparing Clusterings. Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, 2012. (PDF) (Java Implementation of Measures)

Feiping Nie, Zinan Zeng, Ivor W. Tsang, Dong Xu, Changshui Zhang. Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering. IEEE Transactions on Neural Networks, 22(11): 1796 - 1808, Nov 2011. (PDF)
How to do Out-of-Sample Spectral Clustering ?

Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou. Tighter and Convex Maximum Margin Clustering. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, Journal of Machine Learning Research W & CPs, Vol. 5, pp. 344-351, Clearwater Beach, Florida, USA, April 2009. (PDF) (Software) [Plenary Oral, Acceptance rate = 10%]
The mixed integer program in MMC can be solved by a scalable and efficient multiple output-kernel learning algorithm, which achieves a tighter approximation than the SDP relaxation

Kai Zhang, Ivor W. Tsang, James T. Kwok. Maximum Margin Clustering Made Practical. IEEE Transactions on Neural Networks, 20(4): 583-596, April 2009. (PDF) (Software)
CCCP for MMC/TSVM can be shown as alternating optimization
Why do local optimization methods for MMC/TSVM easily get stuck in local minima ?

Ivor W. Tsang, James T. Kwok, Shutao Li. Learning the kernel in Mahalanobis one-class support vector machines. Proceedings of the International Joint Conference on Neural Networks (IJCNN'06), pp.1169- 1175, Vancouver, Canada, July 2006. (PDF)
Relative Margin is introduced in one-class Support Vector Machine to learn an optimal kernel for novelty detection

James T. Kwok, Ivor W. Tsang. The pre-image problem in kernel methods. IEEE Transactions on Neural Networks, 15(6):1517-1525, Nov 2004. (PDF) (Software)
#This paper was awarded with the IEEE Transactions on Neural Networks Outstanding 2004 Paper Award

Qi Mao, Ivor W. Tsang, Shenghua Gao. Objective-guided Image Annotation. IEEE Transactions on Image Processing, 22(4):1585-1597, April 2013. (PDF)
A unified framework of performance metrics is proposed for evaluating various Multi-Label prediction tasks
An efficient algorithm is presented to optimize the proposed performance metrics
Our theoretical analysis and empirical results show that example-based performance measures are more prefered than macro/micro-averaging measures/hamming loss in image annotation tasks, where annotation tags of images follows the distribution of Power Law
Our analysis reveals that macro-averaging measures are very sensitive to infrequent keywords, and hamming measure is easily affected by skewed distributions

Lin Chen, Lixin Duan, Ivor W. Tsang, Dong Xu. Efficient Discriminative Learning of Class Hierarchy for Many Class Prediction. Proceedings of the Asian Conference on Computer Vision (ACCV), 2012. (PDF)
An efficient adaptive algorithm is proposed to speed up the Maximum Separating Margin (MSM) class hierarchy learning for fast many-class prediction

Jianbo Yang, Ivor W. Tsang. Hierarchical Maximum Margin Learning for Multi-Class Classification. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), p. 753-760, Barcelona, Spain, July 2011. (PDF)
We propose the Maximum Separating Margin (MSM) criterion to separate classes and to learn a hierarchical decision tree (representing a discriminating order) for fast many-class prediction via output-kernel learning

Qi Mao, Ivor W. Tsang. Efficient Multi-Template Learning for Structured Prediction. IEEE Transactions on Neural Networks and Learning Systems, 24(2): 248 - 261, Feb 2013. (PDF) (A preliminary version appeared in arXiv)

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong. Transductive Ordinal Regression. IEEE Transactions on Neural Networks and Learning Systems, 23(7):1074 - 1086, July 2012. (PDF)

Chun-Wei Seah, Yew-Soon Ong, Ivor W. Tsang, Siwei Jiang. Pareto Rank Learning in Multi-objective Evolutionary Algorithms. Proceedings of the IEEE World Congress on Computational Intelligence, Congress on Evolutionary Computation, June 2012. (PDF)

Xinxing Xu, Ivor W. Tsang, Dong Xu. Handling Ambiguity via Input-Output Kernel Learning. Proceedings of the IEEE International Conference on Data Mining (ICDM 2012), Dec 2012. [Full Paper, Acceptance rate = 11%]

Wen Li, Lixin Duan, Ivor W. Tsang, Dong Xu. Co-Labeling: A New Multi-view Learning Approach for Ambiguous Problems. Proceedings of the IEEE International Conference on Data Mining (ICDM 2012), Dec 2012. [Full Paper, Acceptance rate = 11%] (PDF)

Chun-Wei Seah, Ivor W. Tsang, Yew-Soon Ong. Healing Sample Selection Bias by Source Classifier Selection. Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM 2011), pp. 577 - 586, Vancouver, Canada, Dec 2011. (PDF) [Full Paper, Acceptance rate = 13%]
How to handle dataset bias - the change of class distribution in training source data and test target data ?
How to alleviate Negative Transfer?
How to efficiently handle large-scale transduction ?
How to select and combine source classifiers for the target domain ?

Lixin Duan, Wen Li, Ivor W. Tsang, Dong Xu. Improving Web Image Search by Bag-based Re-ranking. IEEE Transactions on Image Processing, 20(11):3280--3290, November 2011. (PDF)
The notion of different level of ambiguity in positive bags and negative bags are introduced
How to automatically create noisy label for unlabeled web data ?
How to handle a large amount of label noise in web data ?

Jianbo Yang, Ivor W. Tsang. Hierarchical Maximum Margin Learning for Multi-Class Classification. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), p. 753-760, Barcelona, Spain, July 2011. (PDF)
We propose the Maximum Separating Margin (MSM) criterion to separate classes and to learn a hierarchical decision tree (representing a discriminating order) for fast many-class prediction via output-kernel learning

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. Two-Layer Multiple Kernel Learning. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), Journal of Machine Learning Research W & CPs, Vol. 15, Ft. Lauderdale, FL, USA, April 2011.(PDF)
Should we learn a deeper kernel ?

Yu-Feng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou. A Convex Method for Locating Regions of Interest with Multi-Instance Learning. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2009) , pp.15-30, Bled, Slovenia, September 2009. (PDF) (Software)
Instead of bag prediction only, two convex and scalable Key-Instance SVMs via output-kernel learning are proposed to identify the Key Instance (KI) in positive bags and to predict bags simultaneously
Instance level KI-SVM can accurately locate the region of KI inside a positive bag; while Bag level KI-SVM achieves better bag prediction performance

Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou. Tighter and Convex Maximum Margin Clustering. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, Journal of Machine Learning Research W & CPs, Vol. 5, pp. 344-351, Clearwater Beach, Florida, USA, April 2009. (PDF) (Software) [Plenary Oral, Acceptance rate = 10%]
The mixed integer program in MMC can be solved by a scalable and efficient multiple output-kernel learning algorithm, which achieves a tighter approximation than the SDP relaxation

Xinxing Xu, Ivor W. Tsang, Dong Xu. Soft Margin Multiple Kernel Learning. IEEE Transactions on Neural Networks and Learning Systems, 24(5):749 - 761, May 2013. (PDF)
A soft margin framework for MKL is introduced, which discovers the connections to existing MKLs with various norms, and explains their generalization under different noisy base kernels

Qi Mao, Ivor W. Tsang. Efficient Multi-Template Learning for Structured Prediction. IEEE Transactions on Neural Networks and Learning Systems, 24(2): 248 - 261, Feb 2013. (PDF) (A preliminary version appeared in arXiv)

Lixin Duan, Ivor W. Tsang, Dong Xu. Domain Transfer Multiple Kernel Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):465-479, March 2012. (PDF)

Jinfeng Zhuang, Ivor W. Tsang, Steven C. Hoi. Two-Layer Multiple Kernel Learning. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011), Journal of Machine Learning Research W & CPs, Vol. 15, Ft. Lauderdale, FL, USA, April 2011.(PDF)
Should we learn a deeper kernel ?

Lixin Duan, Ivor W. Tsang, Dong Xu, Stephen J. Maybank. Domain Transfer SVM for Video Concept Detection. Proceedings of the 22rd IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami Beach, Florida, USA, June 2009.(PDF)
Domain Transfer SVM combines both Multiple Kernel Learning (MKL) and Distribution Matching via MMD for Semi-Supervised Learning/Transduction

Ivor W. Tsang, James T. Kwok, Shutao Li. Learning the kernel in Mahalanobis one-class support vector machines. Proceedings of the International Joint Conference on Neural Networks (IJCNN'06), pp.1169- 1175, Vancouver, Canada, July 2006. (PDF)
Relative Margin is introduced in one-class Support Vector Machine to learn an optimal kernel for novelty detection

Qi Mao, Ivor W. Tsang. A Feature Selection Method for Multivariate Performance Measures. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. (PDF)(A preliminary version appeared in arXiv)
How to optimize multivariate performance measures for Multiple-Instance Learning via Embedded instance Selection (MILES) ?

Nan Li, Ivor W. Tsang, Zhi-Hua Zhou. Efficient Optimization of Performance Measures by Classifier Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6): 1370-1382, June 2013. (PDF)
How to efficiently learn a nonlinear classifier for optimizing non-convex and non-smooth performance measures ?

Qi Mao, Ivor W. Tsang, Shenghua Gao. Objective-guided Image Annotation. IEEE Transactions on Image Processing, 22(4):1585-1597, April 2013. (PDF)
A unified framework of performance metrics is proposed for evaluating various Multi-Label prediction tasks
An efficient algorithm is presented to optimize the proposed performance metrics
Our theoretical analysis and empirical results show that example-based performance measures are more prefered than macro/micro-averaging measures/hamming loss in image annotation tasks, where annotation tags of images follows the distribution of Power Law
Our analysis reveals that macro-averaging measures are very sensitive to infrequent keywords, and hamming measure is easily affected by skewed distributions

Qiaoliang Xiang, Qi Mao, Kian Ming Chai, Hai Leong Chieu, Ivor W. Tsang, Zhendong Zhao. A Split-Merge Framework for Comparing Clusterings. Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, 2012. (PDF) (Java Implementation of Measures)

Jian Bo Yang, Qi Mao, Qiao Liang Xiang, Ivor W. Tsang, Kian Ming Adam Chai and Hai Leong Chieu. Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach. Proceedings of the Conference on Empirical Methods in Natural Language Processing and Conference on Natural Language Learning (EMNLP-CoNLL), Jeju, South Korea, July 2012. (PDF)
A domain adaptation algorithm is introduced for coreference resolution (supervised clustering) in resource poor domains
Moreover, we present a general framework to optimize for any user-specified performance measure

Qi Mao, Ivor W. Tsang. Optimizing Performance Measures for Feature Selection. Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM 2011), Vancouver, Canada, Dec 2011. (PDF)
A two-layer cutting plane algorithm together with a primal MKL algorithm are proposed for optimizing performance measures for feature selection where the problem has exponential size of both feature groups and label configurations for a given dataset

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Domain Adaptation in Real World Applications. Tutorial, ACML 2012, Singapore, Nov 2012.

Domain Transfer Learning: Basics, Algorithms and Applications. Invited Talk, Department of Computer Science, KAIST, Daejeon, South Korea, July 2012.

Domain Transfer Learning: Basics and Algorithms. Tutorial, IEEE CVPR 2012, Providence, Rhode Island, June 2012.

Structured Feature Selection for Very High Dimensional Problems. Invited Lecture, Machine Learning Summer School (MLSS 2011), Singapore, June 2011.

Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. Invited Talk, Baidu, Beijing, China, September 2010.

Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets. Invited Talk, Institute for Pure and Applied Mathematics, University of California, Los Angeles , USA, July 2010.

Non-Parametric Kernel Learning: Algorithms and Applications. Seminar, Department of Mathematical Informatics, The University of Tokyo , Tokyo, Japan, July 2010.

Non-Parametric Kernel Learning: Algorithms and Applications. Invited Talk, Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology , Tokyo, Japan, July 2010.

SimpleNPKL: Simple Non-Parametric Kernel Learning. Special Lecture, Department of Computer Science, University of Alberta, Edmonton, Alberta, Canada, June 2009.

Large-Scale Maximum Margin Clustering. Invited Talk, DSO National laboratories, Singapore, April 2009.

Support Vector Machine Made Simpler. LAMDA Open Seminar, Department of Computer Science & Technology, Nanjing University, China, June 2008.

Machine learning on very large data sets. Machine Learning Lunch Seminar, Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, September 2007.

Machine learning on very large data sets. CS Seminar, School of Computing, National University of Singapore, Singapore, March 2007.

Kernel methods meet minimum enclosing balls. Colloquium Presentation, School of Computer Science, Simon Fraser University, Vancouver, Canada, December 2006.

Kernel methods meet minimum enclosing balls. Colloquium Presentation, Department Schoelkopf, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, September 2006.

Kernel methods meet minimum enclosing balls. Colloquium Presentation, Intelligent Data Analysis Group, Fraunhofer FIRST Institute, Berlin, Germany, September 2006.

Very large SVM training using core vector machines. Invited Talk, School of Mathematics, Statistics and Computer Science, University of New England, Armidale, Australia, January 2005.

Efficient hyperkernel learning using second-order cone programming. Invited Talk, School of Mathematics, Statistics and Computer Science, University of New England, Armidale, Australia, January 2005.

Very large SVM training using core vector machines. Invited Talk, School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, January 2005.

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