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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.
| Autorius: | Zhengming Ding, Yun Fu, Handong Zhao, |
| Serija: | Advanced Information and Knowledge Processing |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 280 |
| ISBN-10: | 3030007332 |
| ISBN-13: | 9783030007331 |
| Formatas: | 241 x 160 x 21 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Learning Representation for Multi-View Data Analysis: Models and Applications“
