Learning Representation for Multi-View Data Analysis [E-Book] : Models and Applications / by Zhengming Ding, Handong Zhao, Yun Fu.
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 reader...
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Full text |
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Personal Name(s): | Ding, Zhengming, author |
Fu, Yun, author / Zhao, Handong, author | |
Edition: |
1st edition 2019 |
Imprint: |
Cham :
Springer,
2019
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Physical Description: |
X, 268 pages 76 illustrations, 69 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9783030007348 |
DOI: |
10.1007/978-3-030-00734-8 |
Series Title: |
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Advanced Information and Knowledge Processing
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Subject (LOC): |
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. |