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010 a| 2017446912;

020 a| 9780128127315q| paperback;

020 a| 0128127317q| paperback;

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100 1 a| Lin, Zhouchen.=| ^A1374969;

245 10 a| Low-rank models in visual analysish| [electronic resource] :b| theories, algorithms, and applications /c| Zhouchen Lin, Hongyang Zhang.;

260 a| London :b| Academic Press, an imprint of Elsevier,c| [2017];

300 a| xvi, 243 pages :b| illustrations ;c| 23 cm.;

490 1 a| Computer vision and pattern recognition series;

504 a| Includes bibliographical references and index.;

505 00 g| 1.t| Introduction --t| References --g| 2.t| Linear Models --g| 2.1.t| Single Subspace Models --g| 2.2.t| Multi-Subspace Models --g| 2.3.t| Theoretical Analysis --g| 2.3.1.t| Exact Recovery --g| 2.3.2.t| Closed-Form Solutions --g| 2.3.3.t| Block-Diagonal Structure --t| References --g| 3.t| Nonlinear Models --g| 3.1.t| Kernel Methods --g| 3.2.t| Laplacian Based Methods --g| 3.3.t| Locally Linear Representation --g| 3.4.t| Transformation Invariant Clustering --t| References --g| 4.t| Optimization Algorithms --g| 4.1.t| Convex Algorithms --g| 4.1.1.t| Accelerated Proximal Gradient --g| 4.1.2.t| Frank -- Wolfe Algorithm --g| 4.1.3.t| Alternating Direction Method --g| 4.1.4.t| Linearized Alternating Direction Method with Adaptive Penalty --g| 4.1.5.t| (Proximal) Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty --g| 4.2.t| Nonconvex Algorithms --g| 4.2.1.t| Generalized Singular Value Thresholding --g| 4.2.2.t| Iteratively Reweighted Nuclear Norm Algorithm --g| 4.2.3.t| Truncated Nuclear Norm Minimization --g| 4.2.4.t| Iteratively Reweighted Least Squares --g| 4.2.5.t| Factorization Method --g| 4.3.t| Randomized Algorithms --g| 4.3.1.t| l1 Filtering Algorithm --g| 4.3.2.t| l2, 1 Filtering Algorithm --g| 4.3.3.t| Randomized Algorithm for Relaxed Robust LRR --g| 4.3.4.t| Randomized Algorithm for Online Matrix Completion --t| References --g| 5.t| Representative Applications --g| 5.1.t| Video Denoising [19] --g| 5.1.1.t| Implementation Details --g| 5.1.2.t| Experiments --g| 5.2.t| Background Modeling [2] --g| 5.2.1.t| Implementation Details --g| 5.2.2.t| Experiments --g| 5.3.t| Robust Alignment by Sparse and Low-Rank (RASL) Decomposition [42] --g| 5.3.1.t| Implementation Details --g| 5.3.2.t| Experiments --g| 5.4.t| Transform Invariant Low-Rank Textures (TILT) [58] --g| 5.5.t| Motion and Image Segmentation [30,29,4] --g| 5.6.t| Image Saliency Detection [21] --g| 5.7.t| Partial-Duplicate Image Search [54] --g| 5.7.1.t| Implementation Details --g| 5.7.2.t| Experiments --g| 5.8.t| Image Tag Completion and Refinement [15] --g| 5.8.1.t| Implementation Details --g| 5.8.2.t| Experiments --g| 5.9.t| Other Applications --t| References --g| 6.t| Conclusions --g| 6.1.t| Low-Rank Models for Tensorial Data --g| 6.2.t| Nonlinear Manifold Clustering --g| 6.3.t| Randomized Algorithms --t| References --g| A.t| Proofs --g| A.1.t| Proof of Theorem 2.6 [29] --g| A.1.1.t| Dual Conditions --g| A.1.2.t| Certification by Least Squares --g| A.1.3.t| Proofs of Dual Conditions --g| A.2.t| Proof of Theorem 2.7 [29] --g| A.3.t| Proof of Theorem 2.8 [29] --g| A.3.1.t| Preliminaries --g| A.3.2.t| Exact Recovery of Column Support --g| A.3.3.t| Certification by Golfing Scheme --g| A.3.4.t| Proofs of Dual Conditions --g| A.3.5.t| Exact Recovery of Column Space --g| A.4.t| Proof of Theorem 2.10 [30] --g| A.5.t| Proof of Theorem 2.11 [30] --g| A.6.t| Proof of Theorem 2.12 [30] --g| A.7.t| Proof of Theorem 2.13 [30] --g| A.8.t| Proof of Theorem 2.14 [19] --g| A.9.t| Proof of Theorem 2.15 --g| A.10.t| Proof of Theorem 2.16 [8] --g| A.11.t| Proof of Theorem 2.17 [8] --g| A.12.t| Proof of Theorem 2.18 --g| A.13.t| Proof of Theorem 2.19 [27] --g| A.14.t| Proof of Theorem 2.20 [27] --g| A.15.t| Proof of Theorem 2.21 [27] --g| A.16.t| Proof of Theorem 2.22 [20] --g| A.17.t| Proof of Theorem 4.2 [2] --g| A.18.t| Proof of Theorem 4.4 [15] --g| A.19.t| Proof of Theorem 4.5 [16] --g| A.20.t| Proof of Theorem 4.6 [16] --g| A.21.t| Proofs of Proposition 4.2 and Theorem 4.7 [18] --g| A.22.t| Proof of Theorem 4.8 [17] --g| A.23.t| Proof of Theorem 4.9 [17] --g| A.24.t| Proof of Theorem 4.16 [21] --g| A.25.t| Proof of Theorem 4.17 [21] --g| A.26.t| Proof of Theorem 4.18 [25] --g| A.27.t| Proof of Theorem 4.19 [28] --g| A.28.t| Proof of Theorem 4.21 [1] --g| A.29.t| Proof of Theorem 4.22 [1] --t| References --g| B.t| Mathematical Preliminaries --g| B.1.t| Terminologies --g| B.2.t| Basic Results --t| References.;

506 a| Available only to authorized users.;

538 a| Mode of access: World Wide Web;

650 0 a| Computer vision.=| ^A162076;

650 0 a| Pattern recognition systems.=| ^A37220;

650 0 a| Computer algorithms.=| ^A285321;

650 7 a| Computer algorithms.2| fast0| (OCoLC)fst00872010;

650 7 a| Computer vision.2| fast0| (OCoLC)fst00872687;

650 7 a| Pattern recognition systems.2| fast0| (OCoLC)fst01055266;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Zhang, Hongyang.=| ^A1340959;

830 0 a| Computer vision and pattern recognition series.=| ^A1361494;

856 40 z| Full text available from eBook - Engineering 2017 [EBCE17]u| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Fbook%2F9780128127315;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5244669-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 2/13/2020x| EBOOKz| JERESOURCE;

Low-rank models in visual analysis theories, algorithms, and applications / Zhouchen Lin, Hongyang Zhang.

Author/creator	Lin, Zhouchen
Other author	Zhang, Hongyang.
Format	Electronic
Publication Info	London : Academic Press, an imprint of Elsevier, [2017]
Description	xvi, 243 pages : illustrations ; 23 cm.
Supplemental Content	Full text available from eBook - Engineering 2017 [EBCE17]
Subjects	Computer vision. Pattern recognition systems. Computer algorithms.

Series	Computer vision and pattern recognition series Computer vision and pattern recognition series. ^A1361494
Contents	1. Introduction -- References -- 2. Linear Models -- 2.1. Single Subspace Models -- 2.2. Multi-Subspace Models -- 2.3. Theoretical Analysis -- 2.3.1. Exact Recovery -- 2.3.2. Closed-Form Solutions -- 2.3.3. Block-Diagonal Structure -- References -- 3. Nonlinear Models -- 3.1. Kernel Methods -- 3.2. Laplacian Based Methods -- 3.3. Locally Linear Representation -- 3.4. Transformation Invariant Clustering -- References -- 4. Optimization Algorithms -- 4.1. Convex Algorithms -- 4.1.1. Accelerated Proximal Gradient -- 4.1.2. Frank -- Wolfe Algorithm -- 4.1.3. Alternating Direction Method -- 4.1.4. Linearized Alternating Direction Method with Adaptive Penalty -- 4.1.5. (Proximal) Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty -- 4.2. Nonconvex Algorithms -- 4.2.1. Generalized Singular Value Thresholding -- 4.2.2. Iteratively Reweighted Nuclear Norm Algorithm -- 4.2.3. Truncated Nuclear Norm Minimization -- 4.2.4. Iteratively Reweighted Least Squares -- 4.2.5. Factorization Method -- 4.3. Randomized Algorithms -- 4.3.1. l1 Filtering Algorithm -- 4.3.2. l2, 1 Filtering Algorithm -- 4.3.3. Randomized Algorithm for Relaxed Robust LRR -- 4.3.4. Randomized Algorithm for Online Matrix Completion -- References -- 5. Representative Applications -- 5.1. Video Denoising [19] -- 5.1.1. Implementation Details -- 5.1.2. Experiments -- 5.2. Background Modeling [2] -- 5.2.1. Implementation Details -- 5.2.2. Experiments -- 5.3. Robust Alignment by Sparse and Low-Rank (RASL) Decomposition [42] -- 5.3.1. Implementation Details -- 5.3.2. Experiments -- 5.4. Transform Invariant Low-Rank Textures (TILT) [58] -- 5.5. Motion and Image Segmentation [30,29,4] -- 5.6. Image Saliency Detection [21] -- 5.7. Partial-Duplicate Image Search [54] -- 5.7.1. Implementation Details -- 5.7.2. Experiments -- 5.8. Image Tag Completion and Refinement [15] -- 5.8.1. Implementation Details -- 5.8.2. Experiments -- 5.9. Other Applications -- References -- 6. Conclusions -- 6.1. Low-Rank Models for Tensorial Data -- 6.2. Nonlinear Manifold Clustering -- 6.3. Randomized Algorithms -- References -- A. Proofs -- A.1. Proof of Theorem 2.6 [29] -- A.1.1. Dual Conditions -- A.1.2. Certification by Least Squares -- A.1.3. Proofs of Dual Conditions -- A.2. Proof of Theorem 2.7 [29] -- A.3. Proof of Theorem 2.8 [29] -- A.3.1. Preliminaries -- A.3.2. Exact Recovery of Column Support -- A.3.3. Certification by Golfing Scheme -- A.3.4. Proofs of Dual Conditions -- A.3.5. Exact Recovery of Column Space -- A.4. Proof of Theorem 2.10 [30] -- A.5. Proof of Theorem 2.11 [30] -- A.6. Proof of Theorem 2.12 [30] -- A.7. Proof of Theorem 2.13 [30] -- A.8. Proof of Theorem 2.14 [19] -- A.9. Proof of Theorem 2.15 -- A.10. Proof of Theorem 2.16 [8] -- A.11. Proof of Theorem 2.17 [8] -- A.12. Proof of Theorem 2.18 -- A.13. Proof of Theorem 2.19 [27] -- A.14. Proof of Theorem 2.20 [27] -- A.15. Proof of Theorem 2.21 [27] -- A.16. Proof of Theorem 2.22 [20] -- A.17. Proof of Theorem 4.2 [2] -- A.18. Proof of Theorem 4.4 [15] -- A.19. Proof of Theorem 4.5 [16] -- A.20. Proof of Theorem 4.6 [16] -- A.21. Proofs of Proposition 4.2 and Theorem 4.7 [18] -- A.22. Proof of Theorem 4.8 [17] -- A.23. Proof of Theorem 4.9 [17] -- A.24. Proof of Theorem 4.16 [21] -- A.25. Proof of Theorem 4.17 [21] -- A.26. Proof of Theorem 4.18 [25] -- A.27. Proof of Theorem 4.19 [28] -- A.28. Proof of Theorem 4.21 [1] -- A.29. Proof of Theorem 4.22 [1] -- References -- B. Mathematical Preliminaries -- B.1. Terminologies -- B.2. Basic Results -- References.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2017446912
ISBN	9780128127315 paperback
ISBN	0128127317 paperback

Low-rank models in visual analysis theories, algorithms, and applications / Zhouchen Lin, Hongyang Zhang.

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