A survey of multi-view machine learning

Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning.

[1]  Steven P. Abney,et al.  Bootstrapping , 2002, ACL.

[2]  R. Bharat Rao,et al.  Bayesian Co-Training , 2007, J. Mach. Learn. Res..

[3]  Shiliang Sun,et al.  Hierarchical Multi-view Fisher Discriminant Analysis , 2009, ICONIP.

[4]  Shiliang Sun,et al.  Multi-source Transfer Learning with Multi-view Adaboost , 2012, ICONIP.

[5]  Peter L. Bartlett,et al.  The Rademacher Complexity of Co-Regularized Kernel Classes , 2007, AISTATS.

[6]  Zhi-Hua Zhou,et al.  Analyzing Co-training Style Algorithms , 2007, ECML.

[7]  Shiliang Sun,et al.  Local within-class accuracies for weighting individual outputs in multiple classifier systems , 2010, Pattern Recognit. Lett..

[8]  Shiliang Sun,et al.  Multiple-view multiple-learner active learning , 2010, Pattern Recognit..

[9]  John Shawe-Taylor,et al.  Two view learning: SVM-2K, Theory and Practice , 2005, NIPS.

[10]  Steffen Bickel,et al.  Multi-view clustering , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[11]  Maria-Florina Balcan,et al.  A PAC-Style Model for Learning from Labeled and Unlabeled Data , 2005, COLT.

[12]  Joshua M. Lewis,et al.  Multi-view kernel construction , 2010, Machine Learning.

[13]  Mikhail Belkin,et al.  A Co-Regularization Approach to Semi-supervised Learning with Multiple Views , 2005 .

[14]  Sanjoy Dasgupta,et al.  PAC Generalization Bounds for Co-training , 2001, NIPS.

[15]  Shiliang Sun,et al.  Subspace ensembles for classification , 2007 .

[16]  Shiliang Sun,et al.  View Construction for Multi-view Semi-supervised Learning , 2011, ISNN.

[17]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003 .

[18]  David S. Rosenberg,et al.  Multiview point cloud kernels for semisupervised learning [Lecture Notes] , 2009, IEEE Signal Processing Magazine.

[19]  David S. Rosenberg,et al.  The rademacher complexity of coregularized kernel classes , 2007 .

[20]  Pierre Isabelle,et al.  Proceedings of the 40th Annual Meeting on Association for Computational Linguistics , 2002, ACL 2002.

[21]  John Shawe-Taylor,et al.  Sparse canonical correlation analysis , 2009, Machine Learning.

[22]  Yoram Singer,et al.  Unsupervised Models for Named Entity Classification , 1999, EMNLP.

[23]  Shiliang Sun,et al.  The random electrode selection ensemble for EEG signal classification , 2008, Pattern Recognit..

[24]  Shiliang Sun,et al.  An Algorithm on Multi-View Adaboost , 2010, ICONIP.

[25]  Shiliang Sun,et al.  Active learning with extremely sparse labeled examples , 2010, Neurocomputing.

[26]  Aristidis Likas,et al.  Convex Mixture Models for Multi-view Clustering , 2009, ICANN.

[27]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[28]  Zhi-Hua Zhou,et al.  A New Analysis of Co-Training , 2010, ICML.

[29]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[30]  Peter L. Bartlett,et al.  Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..

[31]  David A. McAllester.  PAC-Bayesian model averaging , 1999, COLT '99.

[32]  Shiliang Sun,et al.  Robust Co-Training , 2011, Int. J. Pattern Recognit. Artif. Intell..

[33]  Hal Daumé,et al.  A Co-training Approach for Multi-view Spectral Clustering , 2011, ICML.

[34]  Bernhard Schölkopf,et al.  Kernel Methods and Support Vector Machines , 2003 .

[35]  Shiliang Sun,et al.  Multi-view Laplacian Support Vector Machines , 2011, ADMA.

[36]  J. Langford.  Tutorial on Practical Prediction Theory for Classification , 2005, J. Mach. Learn. Res..

[37]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[38]  Ulf Brefeld,et al.  Multi-view Discriminative Sequential Learning , 2005, ECML.

[39]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[40]  Shiliang Sun,et al.  Multiple-View Multiple-Learner Semi-Supervised Learning , 2011, Neural Processing Letters.

[41]  Ion Muslea,et al.  Active Learning with Multiple Views , 2006, Encyclopedia of Data Warehousing and Mining.

[42]  Vikas Sindhwani,et al.  An RKHS for multi-view learning and manifold co-regularization , 2008, ICML '08.

[43]  Xi Chen,et al.  Structured Sparse Canonical Correlation Analysis , 2012, AISTATS.

[44]  Shiliang Sun,et al.  Multi-view Transfer Learning with Adaboost , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[45]  John Blitzer,et al.  Co-Training for Domain Adaptation , 2011, NIPS.

[46]  Chong-sun Kim.  Canonical Analysis of Several Sets of Variables , 1973 .

[47]  Shiliang Sun,et al.  Sparse Semi-supervised Learning Using Conjugate Functions , 2010, J. Mach. Learn. Res..

[48]  Ben Taskar,et al.  Multi-View Learning over Structured and Non-Identical Outputs , 2008, UAI.

[49]  Partha Niyogi,et al.  Multiview point cloud kernels for semisupervised learning , 2009 .

[50]  John Shawe-Taylor,et al.  Synthesis of maximum margin and multiview learning using unlabeled data , 2007, ESANN.

[51]  Maria-Florina Balcan,et al.  Co-Training and Expansion: Towards Bridging Theory and Practice , 2004, NIPS.

[52]  Martha White,et al.  Convex Multi-view Subspace Learning , 2012, NIPS.

[53]  John Shawe-Taylor,et al.  Convergence analysis of kernel Canonical Correlation Analysis: theory and practice , 2008, Machine Learning.

[54]  Shiliang Sun,et al.  PAC-bayes bounds with data dependent priors , 2012, J. Mach. Learn. Res..

[55]  Hal Daumé,et al.  Co-regularized Multi-view Spectral Clustering , 2011, NIPS.

[56]  Rayid Ghani,et al.  Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.

[57]  Francis R. Bach,et al.  Sparse probabilistic projections , 2008, NIPS.

[58]  Pei Ling Lai,et al.  Ica Using Kernel Canonical Correlation Analysis , 2000 .

[59]  Qiang Yang,et al.  Semi-Supervised Learning with Very Few Labeled Training Examples , 2007, AAAI.