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Learning Representation for Multi-View Data Analysis : Models and Applications - Handong Zhao

Learning Representation for Multi-View Data Analysis

Models and Applications

By: Handong Zhao, Zhengming Ding, Yun Fu

Hardcover | 17 December 2018

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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.

Industry Reviews
"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)

You Can Find This Book In

Non-FictionComputing & I.T.DatabasesData MiningComputer ScienceArtificial IntelligenceComputer VisionPattern RecognitionExpert Systems & Knowledge-Based SystemsMathematicsProbability & Statistics

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