Learning Representation for Multi-View Data Analysis

Models and Applications

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, General Computing
Cover of the book Learning Representation for Multi-View Data Analysis by Zhengming Ding, Handong Zhao, Yun Fu, Springer International Publishing
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Author: Zhengming Ding, Handong Zhao, Yun Fu ISBN: 9783030007348
Publisher: Springer International Publishing Publication: December 6, 2018
Imprint: Springer Language: English
Author: Zhengming Ding, Handong Zhao, Yun Fu
ISBN: 9783030007348
Publisher: Springer International Publishing
Publication: December 6, 2018
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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