Sun / Mao / Dong Multiview Machine Learning

1. Auflage 2019
ISBN: 978-981-13-3029-2
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 155 Seiten

This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.  


Shiliang Sun received his Ph.D. degree in pattern recognition and intelligent systems from Tsinghua University, Beijing, China, in 2007. He is now a professor at the Department of Computer Science and Technology and the head of the Pattern Recognition and Machine Learning Research Group, East China Normal University, Shanghai, China. His current research interests include multiview learning, kernel methods, learning theory, probabilistic models, approximate inference, and sequential modeling. He has published 150+ research articles at peer-reviewed journals and international conferences. Prof. Sun is on the editorial board of several international journals, including IEEE Transactions on Neural Networks and Learning Systems, Information Fusion, and Pattern Recognition. Liang Mao is a senior Ph.D. student at the Department of Computer Science and Technology and the Pattern Recognition and Machine Learning Research Group, East China Normal University, Shanghai, China. His main research interest is multiview learning and probabilistic models. 

Autoren/Hrsg.

Sun, Shiliang

Mao, Liang

Dong, Ziang

Wu, Lidan

Inhaltsverzeichnis

1;Preface;5
2;Contents;6
3;1 Introduction;10
3.1;1.1 Background;10
3.2;1.2 Definition of Multiview Machine Learning and Related Concepts;10
3.3;1.3 Typical Application Fields in Artificial Intelligence;11
3.4;1.4 Why Can Multiview Learning Be Useful;13
3.5;1.5 Book Structure;14
3.6;References;15
4;2 Multiview Semi-supervised Learning;16
4.1;2.1 Introduction;16
4.2;2.2 Co-training Style Methods;17
4.2.1;2.2.1 Co-training;17
4.2.2;2.2.2 Co-EM;18
4.2.3;2.2.3 Robust Co-training;19
4.3;2.3 Co-regularization Style Methods;21
4.3.1;2.3.1 Co-regularization;21
4.3.2;2.3.2 Bayesian Co-training;23
4.3.3;2.3.3 Multiview Laplacian SVM;25
4.3.4;2.3.4 Multiview Laplacian Twin SVM;27
4.4;2.4 Other Methods;29
4.5;References;31
5;3 Multiview Subspace Learning;32
5.1;3.1 Introduction;32
5.2;3.2 Canonical Correlation Analysis and Related Methods;33
5.2.1;3.2.1 Canonical Correlation Analysis;33
5.2.2;3.2.2 Kernel Canonical Correlation Analysis;35
5.2.3;3.2.3 Probabilistic Canonical Correlation Analysis;37
5.2.4;3.2.4 Bayesian Canonical Correlation Analysis;38
5.3;3.3 Multiview Subspace Learning with Supervision;40
5.3.1;3.3.1 Multiview Linear Discriminant Analysis;40
5.3.2;3.3.2 Multiview Uncorrelated Linear Discriminant Analysis;42
5.3.3;3.3.3 Hierarchical Multiview Fisher Discriminant Analysis;44
5.4;3.4 Other Methods;45
5.5;References;46
6;4 Multiview Supervised Learning;47
6.1;4.1 Introduction;47
6.2;4.2 Multiview Large Margin Classifiers;48
6.2.1;4.2.1 SVM-2K;48
6.2.2;4.2.2 Multiview Maximum Entropy Discriminant;50
6.2.3;4.2.3 Soft Margin-Consistency-Based Multiview Maximum Entropy Discrimination;53
6.3;4.3 Multiple Kernel Learning;56
6.3.1;4.3.1 Kernel Combination;56
6.3.2;4.3.2 Linear Combination of Kernels and Support Kernel Machine;57
6.3.3;4.3.3 SimpleMKL;58
6.4;4.4 Multiview Probabilistic Models;60
6.4.1;4.4.1 Multiview Regularized Gaussian Processes;60
6.4.2;4.4.2 Sparse Multiview Gaussian Processes;61
6.5;4.5 Other Methods;63
6.6;References;64
7;5 Multiview Clustering;66
7.1;5.1 Introduction;66
7.2;5.2 Multiview Spectral Clustering;67
7.2.1;5.2.1 Co-trained Spectral Clustering;67
7.2.2;5.2.2 Co-regularized Spectral Clustering;68
7.3;5.3 Multiview Subspace Clustering;70
7.3.1;5.3.1 Multiview Clustering via Canonical Correlation Analysis;70
7.3.2;5.3.2 Multiview Subspace Clustering;71
7.3.3;5.3.3 Joint Nonnegative Matrix Factorization;73
7.4;5.4 Distributed Multiview Clustering;74
7.5;5.5 Multiview Clustering Ensemble;76
7.6;5.6 Other Methods;76
7.7;References;77
8;6 Multiview Active Learning;79
8.1;6.1 Introduction;79
8.2;6.2 Co-testing;80
8.3;6.3 Bayesian Co-training;81
8.4;6.4 Multiple-View Multiple-Learner;84
8.5;6.5 Active Learning with Extremely Spare Labeled Examples;86
8.6;6.6 Combining Active Learning with Semi-supervising Learning;88
8.7;6.7 Other Methods;90
8.8;References;90
9;7 Multiview Transfer Learning and Multitask Learning;91
9.1;7.1 Introduction;91
9.2;7.2 Multiview Transfer Learning with a Large Margin;92
9.3;7.3 Multiview Discriminant Transfer Learning;94
9.4;7.4 Multiview Transfer Learning with Adaboost;96
9.4.1;7.4.1 Adaboost;97
9.4.2;7.4.2 Multiview Transfer Learning with Adaboost;98
9.4.3;7.4.3 Multisource Transfer Learning with Multiview Adaboost;100
9.5;7.5 Multiview Multitask Learning;101
9.5.1;7.5.1 Graph-Based Interative Multiview Multitask Learning;101
9.5.2;7.5.2 Co-regularized Multiview Multitask Learning Algorithm;104
9.5.3;7.5.3 Convex Shared Structure Learning Algorithm for Multiview Multitask Learning;106
9.6;7.6 Other Methods;108
9.7;References;109
10;8 Multiview Deep Learning;111
10.1;8.1 Introduction;111
10.2;8.2 Joint Representation;112
10.2.1;8.2.1 Probabilistic Graphical Models;112
10.2.2;8.2.2 Fusion of Networks;116
10.2.3;8.2.3 Sequential Models;119
10.3;8.3 Complementary Structured Space;122
10.3.1;8.3.1 Deep Canonical Correlation Analysis;123
10.3.2;8.3.2 Methods Based on Autoencoders;125
10.3.3;8.3.3 Similarity Models;130
10.4;8.4 View Mapping;134
10.4.1;8.4.1 Generative Models;134
10.4.2;8.4.2 Retrieval-Based Methods;138
10.5;References;140
11;9 View Construction;145
11.1;9.1 Introduction;145
11.2;9.2 Feature Set Partition;146
11.2.1;9.2.1 Random Split;147
11.2.2;9.2.2 Genetic Algorithms;147
11.2.3;9.2.3 Pseudo Multiview Co-training;148
11.3;9.3 Purifying;148
11.4;9.4 Noising;150
11.5;9.5 Sequence Reversing;150
11.6;9.6 Multi-module;151
11.7;9.7 Conditional Generative Model;152
11.7.1;9.7.1 Conditional Generative Adversarial Nets;152
11.7.2;9.7.2 Conditional Variational Autoencoders;154
11.8;References;155