Learning Representation for Multi-View Data Analysis: Models and Applications (pdf)

$20.00

Author Ding, Zhengming; Fu, Yun; Zhao, Handong
Edition 1
Edition Year 2019
Format PDF
ISBN 9783030007331
Language English
Number Of Pages 402
Publisher springer

Description

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.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.

Additional information

Author

Ding, Zhengming; Fu, Yun; Zhao, Handong

Edition

1

Edition Year

2019

Format

PDF

ISBN

9783030007331

Language

English

Number Of Pages

402

Publisher

springer

Reviews

There are no reviews yet.

Be the first to review “Learning Representation for Multi-View Data Analysis: Models and Applications (pdf)”

Your email address will not be published. Required fields are marked *