Learning Representation for Multi-View Data Analysis
Ding, Zhengming, Zhao, Handong, Fu, Yun
Produktnummer:
18ec985b76dcbd44d28c7b6d5033f16ada
| Autor: | Ding, Zhengming Fu, Yun Zhao, Handong |
|---|---|
| Themengebiete: | Clustering Deep Learning Matrix Factorization Multi-view Data Subspace Learing Transfer Learning |
| Veröffentlichungsdatum: | 17.12.2018 |
| EAN: | 9783030007331 |
| Sprache: | Englisch |
| Seitenzahl: | 268 |
| Produktart: | Gebunden |
| Verlag: | Springer International Publishing |
| Untertitel: | Models and Applications |
Produktinformationen "Learning Representation for Multi-View Data Analysis"
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.
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