Learning Representation for Multi-View Data Analysis

TRAMA

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.

SOMMARIO

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. 

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783030007331
• Collana: Advanced Information and Knowledge Processing
• Dimensioni: 235 x 155 mm
• Formato: Copertina rigida
• Illustration Notes: X, 268 p. 76 illus., 69 illus. in color.
• Pagine Arabe: 268
• Pagine Romane: x