Erschienen: 21.12.2018 Abbildung von Ding / Zhao / Fu | Learning Representation for Multi-View Data Analysis | 1st ed. 2019 | 2018 | Models and Applications

Ding / Zhao / Fu

Learning Representation for Multi-View Data Analysis

Models and Applications

1st ed. 2019 2018. Buch. x, 268 S. 7 s/w-Abbildungen, 69 Farbabbildungen, Bibliographien. Hardcover

Springer. ISBN 978-3-030-00733-1

Format (B x L): 15,5 x 23,5 cm

Gewicht: 584 g

In englischer Sprache

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.

Autoren

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