Overview
- Broadens your understanding of multi-view data analysis
- Explains how to design an effective multi-view data representation model
- Reinforces multi-view representation principles with real-world practices
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (10 chapters)
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Front Matter
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Unsupervised Multi-view Learning
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Front Matter
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Supervised Multi-view Classification
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Front Matter
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Transfer Learning
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Front Matter
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Keywords
About this book
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.Reviews
Authors and Affiliations
Bibliographic Information
Book Title: Learning Representation for Multi-View Data Analysis
Book Subtitle: Models and Applications
Authors: Zhengming Ding, Handong Zhao, Yun Fu
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/978-3-030-00734-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-00733-1Published: 17 December 2018
eBook ISBN: 978-3-030-00734-8Published: 06 December 2018
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: X, 268
Number of Illustrations: 7 b/w illustrations, 69 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Pattern Recognition