Learning Representation for Multi-view Data Analysis
Language: English
Published by Springer, 2018
Series: Book 58 of 66 - Advanced Information and Knowledge Processing
- Hardcover
- Used

Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Title
- Learning Representation for Multi-view Data Analysis
- Author
- Ding, Zhengming; Zhao, Handong; Fu, Yun
- Publisher
- Springer
- Publication year
- 2018
- Condition
- As New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3030007332
- ISBN 13
- 9783030007331
- Series
- Book 58 of 66: Advanced Information and Knowledge Processing
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."Synopsis" may belong to another edition of this title.
From the Back 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."About the title" may belong to another edition of this title.
GreatBookPricesUK
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