Multiview Machine Learning

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. Preface......Page 3 Contents......Page 4 1.2 Definition of Multiview Machine Learning and Related Concepts......Page 8 1.3 Typical Application Fields in Artificial Intelligence......Page 9 1.4 Why Can Multiview Learning Be Useful......Page 11 1.5 Book Structure......Page 12 References......Page 13 2.1 Introduction......Page 14 2.2.1 Co-training......Page 15 2.2.2 Co-EM......Page 16 2.2.3 Robust Co-training......Page 17 2.3.1 Co-regularization......Page 19 2.3.2 Bayesian Co-training......Page 21 2.3.3 Multiview Laplacian SVM......Page 23 2.3.4 Multiview Laplacian Twin SVM......Page 25 2.4 Other Methods......Page 27 References......Page 29 3.1 Introduction......Page 30 3.2.1 Canonical Correlation Analysis......Page 31 3.2.2 Kernel Canonical Correlation Analysis......Page 33 3.2.3 Probabilistic Canonical Correlation Analysis......Page 35 3.2.4 Bayesian Canonical Correlation Analysis......Page 36 3.3.1 Multiview Linear Discriminant Analysis......Page 38 3.3.2 Multiview Uncorrelated Linear Discriminant Analysis......Page 40 3.3.3 Hierarchical Multiview Fisher Discriminant Analysis......Page 42 3.4 Other Methods......Page 43 References......Page 44 4.1 Introduction......Page 45 4.2.1 SVM-2K......Page 46 4.2.2 Multiview Maximum Entropy Discriminant......Page 48 4.2.3 Soft Margin-Consistency-Based Multiview Maximum Entropy Discrimination......Page 51 4.3.1 Kernel Combination......Page 54 4.3.2 Linear Combination of Kernels and Support Kernel Machine......Page 55 4.3.3 SimpleMKL......Page 56 4.4.1 Multiview Regularized Gaussian Processes......Page 58 4.4.2 Sparse Multiview Gaussian Processes......Page 59 4.5 Other Methods......Page 61 References......Page 62 5.1 Introduction......Page 64 5.2.1 Co-trained Spectral Clustering......Page 65 5.2.2 Co-regularized Spectral Clustering......Page 66 5.3.1 Multiview Clustering via Canonical Correlation Analysis......Page 68 5.3.2 Multiview Subspace Clustering......Page 69 5.3.3 Joint Nonnegative Matrix Factorization......Page 71 5.4 Distributed Multiview Clustering......Page 72 5.6 Other Methods......Page 74 References......Page 75 6.1 Introduction......Page 77 6.2 Co-testing......Page 78 6.3 Bayesian Co-training......Page 79 6.4 Multiple-View Multiple-Learner......Page 82 6.5 Active Learning with Extremely Spare Labeled Examples......Page 84 6.6 Combining Active Learning with Semi-supervising Learning......Page 86 References......Page 88 7.1 Introduction......Page 89 7.2 Multiview Transfer Learning with a Large Margin......Page 90 7.3 Multiview Discriminant Transfer Learning......Page 92 7.4 Multiview Transfer Learning with Adaboost......Page 94 7.4.1 Adaboost......Page 95 7.4.2 Multiview Transfer Learning with Adaboost......Page 96 7.4.3 Multisource Transfer Learning with Multiview Adaboost......Page 98 7.5.1 Graph-Based Interative Multiview Multitask Learning......Page 99 7.5.2 Co-regularized Multiview Multitask Learning Algorithm......Page 102 7.5.3 Convex Shared Structure Learning Algorithm for Multiview Multitask Learning......Page 104 7.6 Other Methods......Page 106 References......Page 107 8.1 Introduction......Page 109 8.2.1 Probabilistic Graphical Models......Page 110 8.2.2 Fusion of Networks......Page 114 8.2.3 Sequential Models......Page 117 8.3 Complementary Structured Space......Page 120 8.3.1 Deep Canonical Correlation Analysis......Page 121 8.3.2 Methods Based on Autoencoders......Page 123 8.3.3 Similarity Models......Page 128 8.4.1 Generative Models......Page 132 8.4.2 Retrieval-Based Methods......Page 136 References......Page 138 9.1 Introduction......Page 143 9.2 Feature Set Partition......Page 144 9.2.2 Genetic Algorithms......Page 145 9.3 Purifying......Page 146 9.5 Sequence Reversing......Page 148 9.6 Multi-module......Page 149 9.7.1 Conditional Generative Adversarial Nets......Page 150 9.7.2 Conditional Variational Autoencoders......Page 152 References......Page 153