Springer Learning Representation for Multi-View Data Analysis: Models and Applications (Advanced Information and Knowledge Processing)

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. Review “The book should be well received by advanced postgraduate students and data (especially big data) analysts. A background in statistics, mathematics, and computing is a prerequisite for reading. It is surely a must-have reference book for any scientific library.” (Soubhik Chakraborty, Computing Reviews, May 07, 2019) From the Back Cover 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.