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.
Rezensionen / Stimmen
"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)
Reihe
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
7 s/w Abbildungen, 69 farbige Abbildungen
X, 268 p. 76 illus., 69 illus. in color.
Dateigröße
ISBN-13
978-3-030-00734-8 (9783030007348)
DOI
10.1007/978-3-030-00734-8
Schweitzer Klassifikation
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.
