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Learning representation for multi-vi...

Ding, Zhengming.

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Learning representation for multi-view data analysis = models and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning representation for multi-view data analysis/ by Zhengming Ding, Handong Zhao, Yun Fu.
Reminder of title:	models and applications /
Author:	Ding, Zhengming.
other author:	Zhao, Handong.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	x, 268 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-00734-8
ISBN:	9783030007348

Learning representation for multi-view data analysis = models and applications /
Ding, Zhengming.

Learning representation for multi-view data analysismodels and applications /[electronic resource] :by Zhengming Ding, Handong Zhao, Yun Fu. - Cham :Springer International Publishing :2019. - x, 268 p. :ill., digital ;24 cm. - Advanced information and knowledge processing,1610-3947. - Advanced information and knowledge processing..

Introduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization.

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.

ISBN: 9783030007348

Standard No.: 10.1007/978-3-030-00734-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Learning representation for multi-view data analysis = models and applications /
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245 1 0 $a Learning representation for multi-view data analysis $h [electronic resource] : $b models and applications / $c by Zhengming Ding, Handong Zhao, Yun Fu.
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300 $a x, 268 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advanced information and knowledge processing, $x 1610-3947
505 0 $a Introduction -- Multi-view Clustering with Complete Information -- Multi-view Clustering with Partial Information -- Multi-view Outlier Detection -- Multi-view Transformation Learning -- Zero-Shot Learning -- Missing Modality Transfer Learning -- Deep Domain Adaptation -- Deep Domain Generalization.
520 $a 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.
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700 1 $a Zhao, Handong. $3 3382412
700 1 $a Fu, Yun. $3 2059227
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