Learning Representation for Multi-View Data Analysis - Models and Applications (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Advanced Information and Knowledge Processing
Release date:	December 2018
First published:	2019
Authors:	Zhengming Ding • Handong Zhao • Yun Fu
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	268
Edition:	1st ed. 2019
ISBN-13:	978-3-03-000733-1
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-000733-2
Barcode:	9783030007331

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