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. Read more...
Abstract:
This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. Read more...
Author(s): Dong, Ziang; Mao, Liang; Sun, Shiliang; Wu, Lidan et al.
Publisher: Springer
Year: 2019
Language: English
Pages: 149
Tags: Machine learning.;Artificial Intelligence.;Pattern Recognition.;Image Processing and Computer Vision.;Data Mining and Knowledge Discovery.;Big Data.
Content: Intro
Preface
Contents
1 Introduction
1.1 Background
1.2 Definition of Multiview Machine Learning and Related Concepts
1.3 Typical Application Fields in Artificial Intelligence
1.4 Why Can Multiview Learning Be Useful
1.5 Book Structure
References
2 Multiview Semi-supervised Learning
2.1 Introduction
2.2 Co-training Style Methods
2.2.1 Co-training
2.2.2 Co-EM
2.2.3 Robust Co-training
2.3 Co-regularization Style Methods
2.3.1 Co-regularization
2.3.2 Bayesian Co-training
2.3.3 Multiview Laplacian SVM
2.3.4 Multiview Laplacian Twin SVM
2.4 Other Methods
References 3 Multiview Subspace Learning3.1 Introduction
3.2 Canonical Correlation Analysis and Related Methods
3.2.1 Canonical Correlation Analysis
3.2.2 Kernel Canonical Correlation Analysis
3.2.3 Probabilistic Canonical Correlation Analysis
3.2.4 Bayesian Canonical Correlation Analysis
3.3 Multiview Subspace Learning with Supervision
3.3.1 Multiview Linear Discriminant Analysis
3.3.2 Multiview Uncorrelated Linear Discriminant Analysis
3.3.3 Hierarchical Multiview Fisher Discriminant Analysis
3.4 Other Methods
References
4 Multiview Supervised Learning
4.1 Introduction 4.2 Multiview Large Margin Classifiers4.2.1 SVM-2K
4.2.2 Multiview Maximum Entropy Discriminant
4.2.3 Soft Margin-Consistency-Based Multiview Maximum Entropy Discrimination
4.3 Multiple Kernel Learning
4.3.1 Kernel Combination
4.3.2 Linear Combination of Kernels and Support Kernel Machine
4.3.3 SimpleMKL
4.4 Multiview Probabilistic Models
4.4.1 Multiview Regularized Gaussian Processes
4.4.2 Sparse Multiview Gaussian Processes
4.5 Other Methods
References
5 Multiview Clustering
5.1 Introduction
5.2 Multiview Spectral Clustering
5.2.1 Co-trained Spectral Clustering 5.2.2 Co-regularized Spectral Clustering5.3 Multiview Subspace Clustering
5.3.1 Multiview Clustering via Canonical Correlation Analysis
5.3.2 Multiview Subspace Clustering
5.3.3 Joint Nonnegative Matrix Factorization
5.4 Distributed Multiview Clustering
5.5 Multiview Clustering Ensemble
5.6 Other Methods
References
6 Multiview Active Learning
6.1 Introduction
6.2 Co-testing
6.3 Bayesian Co-training
6.4 Multiple-View Multiple-Learner
6.5 Active Learning with Extremely Spare Labeled Examples
6.6 Combining Active Learning with Semi-supervising Learning
6.7 Other Methods
