Learning Representation for Multi-View Data Analysis
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Learning Representation for Multi-View Data Analysis
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Description
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.
Product details
- Type:
- Book
- Edition:
- 1
- Languages:
-
Original:
English
- Number of Pages:
-
280
- Publisher:
-
Springer
- Publication Date:
-
2018-12-17
- ISBN / EAN:
-
9783030007331
- Weight:
-
588 g
- Dimensions:
-
16/24/2 cm
- GPSR Manufacturer Reference:
-
[email protected]
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