Description |
1 online resource (xi, 268 pages) |
Series |
Advanced information and knowledge processing |
|
Advanced information and knowledge processing.
|
Contents |
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 |
Summary |
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 |
Bibliography |
Includes bibliographical references |
Notes |
Online resource; title from digital title page (viewed on February 12, 2019) |
Subject |
Machine learning.
|
|
Learning models (Stochastic processes)
|
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Learning models (Stochastic processes)
|
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Machine learning
|
Form |
Electronic book
|
Author |
Zhao, Handong, author
|
|
Fu, Yun, author
|
ISBN |
9783030007348 |
|
3030007340 |
|
9783030007355 |
|
3030007359 |
|