Learning Representation for Multi-View Data Analysis: Models and Applications - Yun Fu,Zhengming Ding,Handong Zhao
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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 we ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Yun Fu, Zhengming Ding, Handong Zhao |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Advanced Information and Knowledge Processing |
| Izlaides gads | 2018 |
| Vāka tips | Cietais vāks |
| EAN | 9783030007331 |