Learning Representation for Multi-view Data Analysis

Ding, Zhengming/ Zhao, Handong/ Fu, Yun

ISBN 10: 3030007332 / ISBN 13: 9783030007331

Editore: Springer-Verlag New York Inc, 2019

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278 pages. 9.25x6.10x0.79 inches. In Stock.

Codice articolo x-3030007332

Dati bibliografici

• Titolo: Learning Representation for Multi-view Data Analysis
• Editore: Springer-Verlag New York Inc
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030007332
• ISBN 13: 9783030007331
• Rilegatura: Hardcover
• Condizione: Brand New
• Peso: 0,57 chilogrammi

Descrizione articolo

Riassunto

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.