Ding Zhengming, Zhao Hadong, Fu Yung. Learning Representation for Multi-View Data Analysis: Models and Applications

Springer, 2019. — 268 p. — ISBN 978-3-030-00733-1.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.What Are Multi-view Data and Problem?
A Unified Perspective
Organization of the Book
Unsupervised Multi-view Learning
Multi-view Clustering with Complete Information
Deep Multi-view Clustering
Ensemble Subspace ClusteringMulti-view Clustering with Partial InformationOverviewIncomplete Multi-view Clustering
Experiment on Synthetic and Real-World DataMulti-view Outlier DetectionRelated Works
Multi-view Outlier Detection Method
Optimization
ExperimentsSupervised Multi-view Classification
Multi-view Transformation Learning
Dual Low-Rank Decomposition for Multi-view Learning
Coupled Marginalized Auto-encoders for Cross-domain Multi-view LearningZero-Shot LearningOverviewThe Proposed Algorithm
ExperimentTransfer Learning
Missing Modality Transfer LearningTransfer Learning via Latent Low-Rank Constraint
ExperimentsMulti-source Transfer LearningOverviewIncomplete Multi-source Transfer Learning
ExperimentsDeep Domain AdaptationStacked Low-Rank Coding
Deep Low-Rank Coding
Spectral Bisection Tree Guided Deep Adaptive Exemplar AutoencoderDeep Domain GeneralizationRelated Work
Deep Generalized Transfer Learning
Experiments