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© 2019

Learning Representation for Multi-View Data Analysis

Models and Applications

Benefits

  • Broadens your understanding of multi-view data analysis

  • Explains how to design an effective multi-view data representation model

  • Reinforces multi-view representation principles with real-world practices

Book

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

  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Northeastern UniversityBostonUSA

Bibliographic information

  • Book Title Learning Representation for Multi-View Data Analysis
  • Book Subtitle Models and Applications
  • Authors Zhengming Ding
    Handong Zhao
    Yun Fu
  • Series Title Advanced Information and Knowledge Processing
  • Series Abbreviated Title Adv. Informat. Knowledge Processing (formerly: KIM-Knowled. Inform. Manag.)
  • 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 Computer Science (R0)
  • Hardcover ISBN 978-3-030-00733-1
  • eBook ISBN 978-3-030-00734-8
  • Series ISSN 1610-3947
  • Series E-ISSN 2197-8441
  • Edition Number 1
  • Number of Pages X, 268
  • Number of Illustrations 7 b/w illustrations, 69 illustrations in colour
  • Topics Data Mining and Knowledge Discovery
    Artificial Intelligence
    Pattern Recognition
  • Buy this book on publisher's site
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Reviews

“The book should be well received by advanced postgraduate students and data (especially big data) analysts. A background in statistics, mathematics, and computing is a prerequisite for reading. It is surely a must-have reference book for any scientific library.” (Soubhik Chakraborty, Computing Reviews, May 07, 2019)