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.
€128.39
विषयसूची
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.
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भाषा अंग्रेज़ी ● स्वरूप PDF ● पेज 268 ● ISBN 9783030007348 ● फाइल का आकार 8.4 MB ● प्रकाशक Springer International Publishing ● शहर Cham ● देश CH ● प्रकाशित 2018 ● डाउनलोड करने योग्य 24 महीने ● मुद्रा EUR ● आईडी 6771308 ● कॉपी सुरक्षा सामाजिक DRM
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