Learning Representation for Multi-View Data Analysis

Models and Applications

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Éditeur :

Springer


Collection :

Advanced Information and Knowledge Processing

Paru le : 2018-12-06



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Description

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.



Pages
268 pages
Collection
Advanced Information and Knowledge Processing
Parution
2018-12-06
Marque
Springer
EAN papier
9783030007331
EAN PDF
9783030007348

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
8172 Ko
Prix
126,59 €
EAN EPUB
9783030007348

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
29635 Ko
Prix
126,59 €