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Book synopsis

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.

Book details

• Full title: Learning Representation for Multi-View Data Analysis
• Author: Zhengming Ding, Handong Zhao, Yun Fu
• Language: English
• Binding: Hardback
• Number of pages: 268
• EAN: 9783030007331
• ISBN: 3030007332
• ID: 20085762
• Publisher: Springer Nature Switzerland AG
• Weight: 588 g
• Dimensions: 235 × 155 × 21 mm
• Date of publishing: 17. December 2018

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