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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.Produkteigenschaften
- Artikelnummer: 9783030007331
- Medium: Buch
- ISBN: 978-3-030-00733-1
- Verlag: Springer International Publishing
- Erscheinungstermin: 17.12.2018
- Sprache(n): Englisch
- Auflage: 1. Auflage 2019
- Serie: Advanced Information and Knowledge Processing
- Produktform: Gebunden
- Gewicht: 588 g
- Seiten: 268
- Format (B x H x T): 160 x 241 x 21 mm
- Ausgabetyp: Kein, Unbekannt