Zhengming Ding, Handong Zhao, Yun Fu

Learning Representation for Multi-View Data Analysis

Models and Applications

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Advanced Information and Knowledge Processing
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-00733-1
• 1st ed. 2019
• Seitenzahl: 280
• Erscheinungstermin: 17. Dezember 2018
• Englisch
• Abmessung: 241mm x 160mm x 21mm
• Gewicht: 566g
• ISBN-13: 9783030007331
• ISBN-10: 3030007332
• Artikelnr.: 53514062

Inhaltsangabe

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.

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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.

Rezensionen

"The book should be well received by advanced postgraduate students and data (especially big data) analysts. A background in statistics, mathematics, and computing is a prerequisite for reading. It is surely a must-have reference book for any scientific library." (Soubhik Chakraborty, Computing Reviews, May 07, 2019)