Learning Representation for Multi-View Data Analysis: Models and Applications
Hardback or Cased Book 268 pages Dec 17, 2018
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Springer
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.
- Author:
- Zhengming Ding
- Publisher:
- Springer
- Publication Date:
- Dec 17, 2018
- Number of pages:
- 268 pages
- Binding:
- Hardback or Cased Book
- ISBN-10:
- 3030007332
- ISBN-13:
- 9783030007331