Learning Representation for Multi-View Data Analysis Models and Applications /
Ding, Zhengming.
Learning Representation for Multi-View Data Analysis Models and Applications / [electronic resource] : by Zhengming Ding, Handong Zhao, Yun Fu. - 1st ed. 2019. - X, 268 p. 76 illus., 69 illus. in color. online resource. - Advanced Information and Knowledge Processing, 2197-8441 . - Advanced Information and Knowledge Processing, .
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. .
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.
9783030007348
10.1007/978-3-030-00734-8 doi
Data mining.
Artificial intelligence.
Pattern recognition systems.
Data Mining and Knowledge Discovery.
Artificial Intelligence.
Automated Pattern Recognition.
QA76.9.D343
006.312
Learning Representation for Multi-View Data Analysis Models and Applications / [electronic resource] : by Zhengming Ding, Handong Zhao, Yun Fu. - 1st ed. 2019. - X, 268 p. 76 illus., 69 illus. in color. online resource. - Advanced Information and Knowledge Processing, 2197-8441 . - Advanced Information and Knowledge Processing, .
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. .
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.
9783030007348
10.1007/978-3-030-00734-8 doi
Data mining.
Artificial intelligence.
Pattern recognition systems.
Data Mining and Knowledge Discovery.
Artificial Intelligence.
Automated Pattern Recognition.
QA76.9.D343
006.312