Multiview Machine Learning

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

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.  

• 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

• Department of Computer Science and Technology, East China Normal University, Shanghai, China

  Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu

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. 

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