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Learning Representation for Multi-View Data Analysis

Models and Applications

  • Zhengming Ding
  • Handong Zhao
  • Yun Fu

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages i-x
  2. Zhengming Ding, Handong Zhao, Yun Fu
    Pages 1-6
  3. Unsupervised Multi-view Learning

    1. Front Matter
      Pages 7-7
    2. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 9-50
    3. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 51-65
    4. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 67-95
  4. Supervised Multi-view Classification

    1. Front Matter
      Pages 97-97
    2. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 99-126
    3. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 127-144
  5. Transfer Learning

    1. Front Matter
      Pages 145-145
    2. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 147-173
    3. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 175-202
    4. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 203-249
    5. Zhengming Ding, Handong Zhao, Yun Fu
      Pages 251-268

About this book

Introduction

This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal.

A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Keywords

Subspace Learing Matrix Factorization Deep Learning Transfer Learning Clustering Multi-view Data

Authors and affiliations

  • Zhengming Ding
    • 1
  • Handong Zhao
    • 2
  • Yun Fu
    • 3
  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Northeastern UniversityBostonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-00734-8
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-00733-1
  • Online ISBN 978-3-030-00734-8
  • Series Print ISSN 1610-3947
  • Series Online ISSN 2197-8441
  • Buy this book on publisher's site
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