Learning Representation for Multi-View Data Analysis: Models and Applications
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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.Format:Hardcover
Language:English
ISBN:3030007332
ISBN13:9783030007331
Release Date:December 2018
Publisher:Springer
Length:268 Pages
Weight:1.25 lbs.
Dimensions:0.7" x 6.1" x 9.2"
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