Learning Representation for Multi-View Data Analysis Models and Applications
Fr. 172.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Gebundene Ausgabe
Erscheinungsdatum
17.12.2018
Abbildungen
X, 268 p. 76 illus., 69 illus. in color.
Verlag
SpringerSeitenzahl
268
Maße (L/B/H)
24.1/16/2.1 cm
Gewicht
588 g
Sprache
Englisch
ISBN
978-3-030-00733-1
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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