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© 2019

Multiview Machine Learning

Book

Table of contents

  1. Front Matter
    Pages i-x
    PDF
  2. Introduction
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 1-6
  3. Multiview Semi-supervised Learning
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 7-22
  4. Multiview Subspace Learning
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 23-37
  5. Multiview Supervised Learning
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 39-57
  6. Multiview Clustering
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 59-71
  7. Multiview Active Learning
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 73-84
  8. Multiview Transfer Learning and Multitask Learning
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 85-104
  9. Multiview Deep Learning
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 105-138
  10. View Construction
    Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu
    Pages 139-149

About this book

Introduction

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.

Keywords

Multiview Learning Data Fusion Data Integration Multi-Modal Learning Machine Learning Semi-Supervised Learning Subspace Learning Clustering Supervised Learning Active Learning Transfer Learning Multitask Learning Deep Learning View Construction

Authors and affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  2. 2.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  3. 3.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  4. 4.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

About the authors

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. 

Bibliographic information