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The Resource Robust representation for data analytics : models and applications, Sheng Li, Yun Fu, (electronic book)

Robust representation for data analytics : models and applications, Sheng Li, Yun Fu, (electronic book)

Label
Robust representation for data analytics : models and applications
Title
Robust representation for data analytics
Title remainder
models and applications
Statement of responsibility
Sheng Li, Yun Fu
Creator
Contributor
Subject
Language
eng
Member of
Cataloging source
YDX
http://library.link/vocab/creatorName
Li, Sheng
Dewey number
006.3/32
Index
index present
LC call number
Q387
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Fu, Yun
Series statement
Advanced information and knowledge processing,
http://library.link/vocab/subjectName
  • Knowledge representation (Information theory)
  • Big data
Label
Robust representation for data analytics : models and applications, Sheng Li, Yun Fu, (electronic book)
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Contents
  • Preface; Contents; 1 Introduction; 1.1 What Are Robust Data Representations?; 1.2 Organization of the Book; Part I Robust Representation Models; 2 Fundamentals of Robust Representations; 2.1 Representation Learning Models; 2.1.1 Subspace Learning; 2.1.2 Multi-view Subspace Learning; 2.1.3 Dictionary Learning; 2.2 Robust Representation Learning; 2.2.1 Subspace Clustering; 2.2.2 Low-Rank Modeling; References; 3 Robust Graph Construction; 3.1 Overview; 3.2 Existing Graph Construction Methods; 3.2.1 Unbalanced Graphs and Balanced Graph; 3.2.2 Sparse Representation Based Graphs
  • 3.2.3 Low-Rank Learning Based Graphs3.3 Low-Rank Coding Based Unbalanced Graph Construction; 3.3.1 Motivation; 3.3.2 Problem Formulation; 3.3.3 Optimization; 3.3.4 Complexity Analysis; 3.3.5 Discussions; 3.4 Low-Rank Coding Based Balanced Graph Construction; 3.4.1 Motivation and Formulation; 3.4.2 Optimization; 3.5 Learning with Graphs; 3.5.1 Graph Based Clustering; 3.5.2 Transductive Semi-supervised Classification; 3.5.3 Inductive Semi-supervised Classification; 3.6 Experiments; 3.6.1 Databases and Settings; 3.6.2 Spectral Clustering with Graph
  • 3.6.3 Semi-supervised Classification with Graph3.6.4 Discussions; 3.7 Summary; References; 4 Robust Subspace Learning; 4.1 Overview; 4.2 Supervised Regularization Based Robust Subspace (SRRS); 4.2.1 Problem Formulation; 4.2.2 Theoretical Analysis; 4.2.3 Optimization; 4.2.3.1 Learn Subspace P on Fixed Low-Rank Representations; 4.2.3.2 Learn Low-Rank Representations Z on Fixed Subspace; 4.2.4 Algorithm and Discussions; 4.3 Experiments; 4.3.1 Object Recognition with Pixel Corruption; 4.3.2 Face Recognition with Illumination and Pose Variation; 4.3.3 Face Recognition with Occlusions
  • 4.3.4 Kinship Verification4.3.5 Discussions; 4.4 Summary; References; 5 Robust Multi-view Subspace Learning; 5.1 Overview; 5.2 Problem Definition; 5.3 Multi-view Discriminative Bilinear Projection (MDBP); 5.3.1 Motivation; 5.3.2 Formulation of MDBP; 5.3.2.1 Learning Shared Representations Across Views; 5.3.2.2 Incorporating Discriminative Regularization; 5.3.2.3 Modeling Temporal Smoothness; 5.3.2.4 Objective Function; 5.3.3 Optimization Algorithm; 5.3.3.1 Time Complexity Analysis; 5.3.4 Comparison with Existing Methods; 5.4 Experiments; 5.4.1 UCI Daily and Sports Activity Dataset
  • 5.4.1.1 Two-View Setting5.4.1.2 Baselines; 5.4.1.3 Classification Scheme; 5.4.1.4 Results; 5.4.2 Multimodal Spoken Word Dataset; 5.4.2.1 Three-View Setting; 5.4.2.2 Results; 5.4.3 Discussions; 5.4.3.1 Parameter Sensitivity and Convergence; 5.4.3.2 Experiments with Data Fusion and Feature Fusion; 5.5 Summary; References; 6 Robust Dictionary Learning; 6.1 Overview; 6.2 Self-Taught Low-Rank (S-Low) Coding ; 6.2.1 Motivation; 6.2.2 Problem Formulation; 6.2.3 Optimization; 6.2.4 Algorithm and Discussions; 6.3 Learning with S-Low Coding; 6.3.1 S-Low Clustering; 6.3.2 S-Low Classification
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9783319601762
Specific material designation
remote
Label
Robust representation for data analytics : models and applications, Sheng Li, Yun Fu, (electronic book)
Publication
Bibliography note
Includes bibliographical references and index
Contents
  • Preface; Contents; 1 Introduction; 1.1 What Are Robust Data Representations?; 1.2 Organization of the Book; Part I Robust Representation Models; 2 Fundamentals of Robust Representations; 2.1 Representation Learning Models; 2.1.1 Subspace Learning; 2.1.2 Multi-view Subspace Learning; 2.1.3 Dictionary Learning; 2.2 Robust Representation Learning; 2.2.1 Subspace Clustering; 2.2.2 Low-Rank Modeling; References; 3 Robust Graph Construction; 3.1 Overview; 3.2 Existing Graph Construction Methods; 3.2.1 Unbalanced Graphs and Balanced Graph; 3.2.2 Sparse Representation Based Graphs
  • 3.2.3 Low-Rank Learning Based Graphs3.3 Low-Rank Coding Based Unbalanced Graph Construction; 3.3.1 Motivation; 3.3.2 Problem Formulation; 3.3.3 Optimization; 3.3.4 Complexity Analysis; 3.3.5 Discussions; 3.4 Low-Rank Coding Based Balanced Graph Construction; 3.4.1 Motivation and Formulation; 3.4.2 Optimization; 3.5 Learning with Graphs; 3.5.1 Graph Based Clustering; 3.5.2 Transductive Semi-supervised Classification; 3.5.3 Inductive Semi-supervised Classification; 3.6 Experiments; 3.6.1 Databases and Settings; 3.6.2 Spectral Clustering with Graph
  • 3.6.3 Semi-supervised Classification with Graph3.6.4 Discussions; 3.7 Summary; References; 4 Robust Subspace Learning; 4.1 Overview; 4.2 Supervised Regularization Based Robust Subspace (SRRS); 4.2.1 Problem Formulation; 4.2.2 Theoretical Analysis; 4.2.3 Optimization; 4.2.3.1 Learn Subspace P on Fixed Low-Rank Representations; 4.2.3.2 Learn Low-Rank Representations Z on Fixed Subspace; 4.2.4 Algorithm and Discussions; 4.3 Experiments; 4.3.1 Object Recognition with Pixel Corruption; 4.3.2 Face Recognition with Illumination and Pose Variation; 4.3.3 Face Recognition with Occlusions
  • 4.3.4 Kinship Verification4.3.5 Discussions; 4.4 Summary; References; 5 Robust Multi-view Subspace Learning; 5.1 Overview; 5.2 Problem Definition; 5.3 Multi-view Discriminative Bilinear Projection (MDBP); 5.3.1 Motivation; 5.3.2 Formulation of MDBP; 5.3.2.1 Learning Shared Representations Across Views; 5.3.2.2 Incorporating Discriminative Regularization; 5.3.2.3 Modeling Temporal Smoothness; 5.3.2.4 Objective Function; 5.3.3 Optimization Algorithm; 5.3.3.1 Time Complexity Analysis; 5.3.4 Comparison with Existing Methods; 5.4 Experiments; 5.4.1 UCI Daily and Sports Activity Dataset
  • 5.4.1.1 Two-View Setting5.4.1.2 Baselines; 5.4.1.3 Classification Scheme; 5.4.1.4 Results; 5.4.2 Multimodal Spoken Word Dataset; 5.4.2.1 Three-View Setting; 5.4.2.2 Results; 5.4.3 Discussions; 5.4.3.1 Parameter Sensitivity and Convergence; 5.4.3.2 Experiments with Data Fusion and Feature Fusion; 5.5 Summary; References; 6 Robust Dictionary Learning; 6.1 Overview; 6.2 Self-Taught Low-Rank (S-Low) Coding ; 6.2.1 Motivation; 6.2.2 Problem Formulation; 6.2.3 Optimization; 6.2.4 Algorithm and Discussions; 6.3 Learning with S-Low Coding; 6.3.1 S-Low Clustering; 6.3.2 S-Low Classification
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9783319601762
Specific material designation
remote

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