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Learning Representation for Multi-View Data Analysis

Models and Applications

Yun Fu author Zhengming Ding author Handong Zhao author

Format:Hardback

Publisher:Springer Nature Switzerland AG

Published:17th Dec '18

Currently unavailable, and unfortunately no date known when it will be back

Learning Representation for Multi-View Data Analysis cover

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.

“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)

ISBN: 9783030007331

Dimensions: unknown

Weight: unknown

268 pages

1st ed. 2019