Dictionary learning algorithms and applications. ([2018])
- Record Type:
- Book
- Title:
- Dictionary learning algorithms and applications. ([2018])
- Main Title:
- Dictionary learning algorithms and applications
- Further Information:
- Note: Bogdan Dumitrescu, Paul Irofti.
- Authors:
- Dumitrescu, Bogdan
Irofti, Paul - Contents:
- Intro; Preface; Contents; 1 Sparse Representations; 1.1 The Sparse Model; 1.2 Algorithms; 1.3 Orthogonal Matching Pursuit; 1.4 Algorithms for Basis Pursuit: FISTA; 1.5 Guarantees; 1.6 The Choice of a Dictionary: Fixed vs Learned; Problems; 2 Dictionary Learning Problem; 2.1 The Optimization Problem; 2.2 An Analysis of the DL Problem; 2.3 Test Problems; 2.3.1 Representation Error; 2.3.2 Dictionary Recovery; 2.4 Applications: A Quick Overview; 2.4.1 Denoising; 2.4.2 Inpainting; 2.4.3 Compression; 2.4.4 Compressed Sensing; 2.4.5 Classification; Problems; 3 Standard Algorithms 3.1 Basic Strategy: Alternating Optimization3.2 Sparse Coding; 3.3 Simple Descent Methods; 3.3.1 Gradient Descent; 3.3.2 Coordinate Descent; 3.4 Method of Optimal Directions (MOD); 3.5 K-SVD; 3.6 Parallel Algorithms; 3.7 SimCO; 3.8 Refinements; 3.9 Practical Issues; 3.9.1 Initialization; 3.9.2 Dictionary Size and Other Size Parameters; 3.9.3 Unused or Redundant Atoms; 3.9.4 Randomization; 3.10 Comparisons: Theory; 3.11 Comparisons: Some Experimental Results; 3.11.1 Representation Error Results; 3.11.2 Dictionary Recovery Result; 3.11.3 Denoising Results 3.12 Impact of Sparse Representation AlgorithmProblems; 4 Regularization and Incoherence; 4.1 Learning with a Penalty; 4.2 Regularization; 4.2.1 Sparse Coding; 4.2.2 Regularized K-SVD; 4.2.3 Comparison Between Regularized K-SVD and SimCO; 4.3 Frames; 4.4 Joint Optimization of Error and Coherence; 4.5 Optimizing an Orthogonal Dictionary; 4.6 ImposingIntro; Preface; Contents; 1 Sparse Representations; 1.1 The Sparse Model; 1.2 Algorithms; 1.3 Orthogonal Matching Pursuit; 1.4 Algorithms for Basis Pursuit: FISTA; 1.5 Guarantees; 1.6 The Choice of a Dictionary: Fixed vs Learned; Problems; 2 Dictionary Learning Problem; 2.1 The Optimization Problem; 2.2 An Analysis of the DL Problem; 2.3 Test Problems; 2.3.1 Representation Error; 2.3.2 Dictionary Recovery; 2.4 Applications: A Quick Overview; 2.4.1 Denoising; 2.4.2 Inpainting; 2.4.3 Compression; 2.4.4 Compressed Sensing; 2.4.5 Classification; Problems; 3 Standard Algorithms 3.1 Basic Strategy: Alternating Optimization3.2 Sparse Coding; 3.3 Simple Descent Methods; 3.3.1 Gradient Descent; 3.3.2 Coordinate Descent; 3.4 Method of Optimal Directions (MOD); 3.5 K-SVD; 3.6 Parallel Algorithms; 3.7 SimCO; 3.8 Refinements; 3.9 Practical Issues; 3.9.1 Initialization; 3.9.2 Dictionary Size and Other Size Parameters; 3.9.3 Unused or Redundant Atoms; 3.9.4 Randomization; 3.10 Comparisons: Theory; 3.11 Comparisons: Some Experimental Results; 3.11.1 Representation Error Results; 3.11.2 Dictionary Recovery Result; 3.11.3 Denoising Results 3.12 Impact of Sparse Representation AlgorithmProblems; 4 Regularization and Incoherence; 4.1 Learning with a Penalty; 4.2 Regularization; 4.2.1 Sparse Coding; 4.2.2 Regularized K-SVD; 4.2.3 Comparison Between Regularized K-SVD and SimCO; 4.3 Frames; 4.4 Joint Optimization of Error and Coherence; 4.5 Optimizing an Orthogonal Dictionary; 4.6 Imposing Explicit Coherence Bounds; 4.7 Atom-by-Atom Decorrelation; Problems; 5 Other Views on the DL Problem; 5.1 Representations with Variable Sparsity Levels; 5.2 A Simple Algorithm for DL with l1 Penalty; 5.3 A Majorization Algorithm 5.4 Proximal Methods5.5 A Gallery of Objectives; 5.6 Task-Driven DL; 5.7 Dictionary Selection; 5.8 Online DL; 5.8.1 Online Coordinate Descent; 5.8.2 RLS DL; 5.9 DL with Incomplete Data; Problems; 6 Optimizing Dictionary Size; 6.1 Introduction: DL with Imposed Error; 6.2 A General Size-Optimizing DL Structure; 6.3 Stagewise K-SVD; 6.4 An Initialization Method; 6.5 An Atom Splitting Procedure; 6.6 Clustering as a DL Tool; 6.7 Other Methods; 6.8 Size-Reducing OMP; Problems; 7 Structured Dictionaries; 7.1 Short Introduction; 7.2 Sparse Dictionaries; 7.2.1 Double Sparsity; 7.2.2 Greedy Selection 7.2.3 Multi-Layer Sparse DL7.2.4 Multiscale Dictionaries; 7.3 Orthogonal Blocks; 7.3.1 Orthogonal Basis Training; 7.3.2 Union of Orthonormal Bases; 7.3.3 Single Block Orthogonal DL; 7.4 Shift Invariant Dictionaries; 7.4.1 Circulant Dictionaries; 7.4.2 Convolutional Sparse Coding; 7.5 Separable Dictionaries; 7.5.1 2D-OMP; 7.5.2 SeDiL; 7.6 Tensor Strategies; 7.6.1 CP Decomposition; 7.6.2 CP Dictionary Update; 7.6.3 Tensor Singular Valued Decomposition; 7.6.4 t-SVD Dictionary Update; 7.7 Composite Dictionaries; 7.7.1 Convex Approach; 7.7.2 Composite Dictionaries with Orthogonal Blocks; Problems … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2018
- Copyright Date:
- 2018
- Extent:
- 1 online resource (xiv, 284 pages), illustrations
- Subjects:
- 518.1
Engineering
Algorithms
Computer algorithms
Composite applications (Computer science)
MATHEMATICS / Numerical Analysis
Algorithms
Composite applications (Computer science)
Computer algorithms
Engineering
Signal, Image and Speech Processing
Mathematical and Computational Engineering
Circuits and Systems
Computer Communication Networks
Information Systems and Communication Service
Mathematics -- Applied
Technology & Engineering -- Electronics -- Circuits -- General
Computers -- Hardware -- Network Hardware
Computers -- Online Services -- General
Maths for engineers
Circuits & components
Network hardware
Computer networking & communications
Engineering mathematics
Systems engineering
Computer Communication Networks
Information systems
Technology & Engineering -- Electronics -- General
Imaging systems & technology
Electronic books - Languages:
- English
- ISBNs:
- 9783319786742
3319786741 - Related ISBNs:
- 9783319786735
3319786733 - Notes:
- Note: Includes bibliographical references and index.
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