Zhengming Ding, Handong Zhao, Yun Fu

Learning Representation for Multi-View Data Analysis (eBook, PDF)

Models and Applications

• Format: PDF

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 268
• Erscheinungstermin: 6. Dezember 2018
• Englisch
• ISBN-13: 9783030007348
• Artikelnr.: 54735511

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.

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)