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TitleLearning Representation for Multi-View Data Analysis [electronic resource] : Models and Applications / by Zhengming Ding, Handong Zhao, Yun Fu
ImprintCham : Springer International Publishing : Imprint: Springer, 2019
Connect tohttps://doi.org/10.1007/978-3-030-00734-8
Descript X, 268 p. 76 illus., 69 illus. in color. online resource

SUMMARY

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


CONTENT

Introduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization.


Data mining Artificial intelligence Optical pattern recognition Data Mining and Knowledge Discovery. http://scigraph.springernature.com/things/product-market-codes/I18030 Artificial Intelligence. http://scigraph.springernature.com/things/product-market-codes/I21000 Pattern Recognition. http://scigraph.springernature.com/things/product-market-codes/I2203X



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