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Learning Representation for Multi-View Data Analysis
  • Learning Representation for Multi-View Data Analysis
Learning Representation for Multi-View Data Analysis: Models and Applications

Learning Representation for Multi-View Data Analysis: Models and Applications

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Learning Representation for Multi-View Data Analysis: Models and Applications

Ding, Zhengming; Zhao, Handong; Fu, Yun

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.

Details

Published by: Springer

Publication Date: 2018-12-17

Format: Hardcover

ISBN-13: 9783030007331

DOI: 10.1007/978-3-030-00734-8

Dimensions: 235.0cm x155.0cm

Pages: 268.0

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