Low-rank models in visual analysis : theories, algorithms, and applications
2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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 Opti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lin, Zhouchen [verfasserIn] Zhang, Hongyang [verfasserIn] |
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
London: Academic Press, an imprint of Elsevier ; 2017 ©2017 |
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| Schlagwörter: | |
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| Formangabe: |
Electronic book Electronic books |
| Anmerkung: |
Includes bibliographical references and index. - Vendor-supplied metadata |
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| Umfang: |
Online Ressource |
| Reproduktion: |
Online-Ausg. |
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| Reihe: |
Computer vision and pattern recognition series |
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| Links: | |
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| ISBN: |
978-0-12-812732-2 0-12-812732-5 |
| Katalog-ID: |
1655313010 |
|---|
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| 245 | 1 | 0 | |a Low-rank models in visual analysis |b theories, algorithms, and applications |c Zhouchen Lin, Hongyang Zhang |
| 264 | 1 | |a London |b Academic Press, an imprint of Elsevier |c 2017 | |
| 264 | 4 | |c ©2017 | |
| 300 | |a Online Ressource | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
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| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 490 | 0 | |a Computer vision and pattern recognition series | |
| 500 | |a Includes bibliographical references and index. - Vendor-supplied metadata | ||
| 520 | |a 2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis | ||
| 520 | |a 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering | ||
| 520 | |a 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 5.1.2 Experiments | ||
| 520 | |a 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix | ||
| 520 | |a Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR | ||
| 533 | |a Online-Ausg. | ||
| 650 | 0 | |a Computer vision | |
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9780128127322 978-0-12-812732-2 0128127325 0-12-812732-5 9780128127315 0128127317 (DE-627)1655313010 (DE-576)517990857 (DE-599)BSZ517990857 (OCoLC)989872332 (ELSEVIER)ocn989872332 (EBP)056062613 DE-627 ger DE-627 rakwb eng XA-GB TA1634 COM000000 bisacsh COM 000000 bisacsh Low-rank models in visual analysis theories, algorithms, and applications Zhouchen Lin, Hongyang Zhang London Academic Press, an imprint of Elsevier 2017 ©2017 Online Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computer vision and pattern recognition series Includes bibliographical references and index. - Vendor-supplied metadata 2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Online-Ausg. Computer vision Pattern recognition systems Computer algorithms Algorithms COMPUTERS ; General Computer algorithms Computer vision Pattern recognition systems Vision par ordinateur (CaQQLa)201-0074889 Algorithms (OCoLC)fst00805020 Reconnaissance des formes (Informatique) (CaQQLa)201-0028094 Algorithmes (CaQQLa)201-0001230 algorithms (CStmoGRI)aat300065585 Electronic book Electronic books Electronic books Lin, Zhouchen verfasserin aut Zhang, Hongyang verfasserin (DE-627)1471656799 (DE-576)401656799 aut 9780128127315 0128127317 0128127317 9780128127315 Erscheint auch als Druck-Ausgabe Lin, Zhouchen Low-rank models in visual analysis London : Academic Press, an imprint of Elsevier, [2017] 0128127317 9780128127315 http://www.sciencedirect.com/science/book/9780128127315 Verlag Volltext https://www.sciencedirect.com/science/book/9780128127315 X:ELSEVIER Verlag lizenzpflichtig BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 006.37 045F 006.3/7 105 01 0841 4074533758 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499995299 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514691313 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540277510 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. 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| spelling |
9780128127322 978-0-12-812732-2 0128127325 0-12-812732-5 9780128127315 0128127317 (DE-627)1655313010 (DE-576)517990857 (DE-599)BSZ517990857 (OCoLC)989872332 (ELSEVIER)ocn989872332 (EBP)056062613 DE-627 ger DE-627 rakwb eng XA-GB TA1634 COM000000 bisacsh COM 000000 bisacsh Low-rank models in visual analysis theories, algorithms, and applications Zhouchen Lin, Hongyang Zhang London Academic Press, an imprint of Elsevier 2017 ©2017 Online Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computer vision and pattern recognition series Includes bibliographical references and index. - Vendor-supplied metadata 2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Online-Ausg. Computer vision Pattern recognition systems Computer algorithms Algorithms COMPUTERS ; General Computer algorithms Computer vision Pattern recognition systems Vision par ordinateur (CaQQLa)201-0074889 Algorithms (OCoLC)fst00805020 Reconnaissance des formes (Informatique) (CaQQLa)201-0028094 Algorithmes (CaQQLa)201-0001230 algorithms (CStmoGRI)aat300065585 Electronic book Electronic books Electronic books Lin, Zhouchen verfasserin aut Zhang, Hongyang verfasserin (DE-627)1471656799 (DE-576)401656799 aut 9780128127315 0128127317 0128127317 9780128127315 Erscheint auch als Druck-Ausgabe Lin, Zhouchen Low-rank models in visual analysis London : Academic Press, an imprint of Elsevier, [2017] 0128127317 9780128127315 http://www.sciencedirect.com/science/book/9780128127315 Verlag Volltext https://www.sciencedirect.com/science/book/9780128127315 X:ELSEVIER Verlag lizenzpflichtig BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 006.37 045F 006.3/7 105 01 0841 4074533758 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499995299 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514691313 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540277510 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520356679 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 404604506X 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 http://www.sciencedirect.com/science/book/9780128127315 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128127315 185 01 3519 http://www.sciencedirect.com/science/book/9780128127315 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128127315 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128127315 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128127315 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfields_unstemmed |
9780128127322 978-0-12-812732-2 0128127325 0-12-812732-5 9780128127315 0128127317 (DE-627)1655313010 (DE-576)517990857 (DE-599)BSZ517990857 (OCoLC)989872332 (ELSEVIER)ocn989872332 (EBP)056062613 DE-627 ger DE-627 rakwb eng XA-GB TA1634 COM000000 bisacsh COM 000000 bisacsh Low-rank models in visual analysis theories, algorithms, and applications Zhouchen Lin, Hongyang Zhang London Academic Press, an imprint of Elsevier 2017 ©2017 Online Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computer vision and pattern recognition series Includes bibliographical references and index. - Vendor-supplied metadata 2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Online-Ausg. Computer vision Pattern recognition systems Computer algorithms Algorithms COMPUTERS ; General Computer algorithms Computer vision Pattern recognition systems Vision par ordinateur (CaQQLa)201-0074889 Algorithms (OCoLC)fst00805020 Reconnaissance des formes (Informatique) (CaQQLa)201-0028094 Algorithmes (CaQQLa)201-0001230 algorithms (CStmoGRI)aat300065585 Electronic book Electronic books Electronic books Lin, Zhouchen verfasserin aut Zhang, Hongyang verfasserin (DE-627)1471656799 (DE-576)401656799 aut 9780128127315 0128127317 0128127317 9780128127315 Erscheint auch als Druck-Ausgabe Lin, Zhouchen Low-rank models in visual analysis London : Academic Press, an imprint of Elsevier, [2017] 0128127317 9780128127315 http://www.sciencedirect.com/science/book/9780128127315 Verlag Volltext https://www.sciencedirect.com/science/book/9780128127315 X:ELSEVIER Verlag lizenzpflichtig BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 006.37 045F 006.3/7 105 01 0841 4074533758 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499995299 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514691313 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540277510 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520356679 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 404604506X 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 http://www.sciencedirect.com/science/book/9780128127315 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128127315 185 01 3519 http://www.sciencedirect.com/science/book/9780128127315 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128127315 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128127315 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128127315 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfieldsGer |
9780128127322 978-0-12-812732-2 0128127325 0-12-812732-5 9780128127315 0128127317 (DE-627)1655313010 (DE-576)517990857 (DE-599)BSZ517990857 (OCoLC)989872332 (ELSEVIER)ocn989872332 (EBP)056062613 DE-627 ger DE-627 rakwb eng XA-GB TA1634 COM000000 bisacsh COM 000000 bisacsh Low-rank models in visual analysis theories, algorithms, and applications Zhouchen Lin, Hongyang Zhang London Academic Press, an imprint of Elsevier 2017 ©2017 Online Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computer vision and pattern recognition series Includes bibliographical references and index. - Vendor-supplied metadata 2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Online-Ausg. Computer vision Pattern recognition systems Computer algorithms Algorithms COMPUTERS ; General Computer algorithms Computer vision Pattern recognition systems Vision par ordinateur (CaQQLa)201-0074889 Algorithms (OCoLC)fst00805020 Reconnaissance des formes (Informatique) (CaQQLa)201-0028094 Algorithmes (CaQQLa)201-0001230 algorithms (CStmoGRI)aat300065585 Electronic book Electronic books Electronic books Lin, Zhouchen verfasserin aut Zhang, Hongyang verfasserin (DE-627)1471656799 (DE-576)401656799 aut 9780128127315 0128127317 0128127317 9780128127315 Erscheint auch als Druck-Ausgabe Lin, Zhouchen Low-rank models in visual analysis London : Academic Press, an imprint of Elsevier, [2017] 0128127317 9780128127315 http://www.sciencedirect.com/science/book/9780128127315 Verlag Volltext https://www.sciencedirect.com/science/book/9780128127315 X:ELSEVIER Verlag lizenzpflichtig BSZ-33-EBS-HSAA GBV-33-EBS-MRI GBV-33-EBS-ZHB GBV-33-Freedom 2021 ZDB-33-EBS ZDB-33-EGE 2017 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_105 ISIL_DE-841 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2111 ISIL_DE-944 BO 045F 006.37 045F 006.3/7 105 01 0841 4074533758 OLR-ELV-TEST Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. Testzugang ZHB Lübeck z 26-02-22 132 01 0959 4499995299 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514691313 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540277510 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 4520356679 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2111 02 DE-944 404604506X 00 --%%-- E-Book Elsevier --%%-- n Elektronischer Volltext - Campuslizenz l01 27-01-22 105 01 0841 http://www.sciencedirect.com/science/book/9780128127315 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128127315 185 01 3519 http://www.sciencedirect.com/science/book/9780128127315 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128127315 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128127315 2111 02 DE-944 https://www.sciencedirect.com/science/book/9780128127315 132 01 0959 00 EBooks Elsevier Engineering 105 01 0841 OLR-ELV-TEST 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
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9780128127322 978-0-12-812732-2 0128127325 0-12-812732-5 9780128127315 0128127317 (DE-627)1655313010 (DE-576)517990857 (DE-599)BSZ517990857 (OCoLC)989872332 (ELSEVIER)ocn989872332 (EBP)056062613 DE-627 ger DE-627 rakwb eng XA-GB TA1634 COM000000 bisacsh COM 000000 bisacsh Low-rank models in visual analysis theories, algorithms, and applications Zhouchen Lin, Hongyang Zhang London Academic Press, an imprint of Elsevier 2017 ©2017 Online Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computer vision and pattern recognition series Includes bibliographical references and index. - Vendor-supplied metadata 2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Online-Ausg. 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2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Includes bibliographical references and index. - Vendor-supplied metadata |
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2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Includes bibliographical references and index. - Vendor-supplied metadata |
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2.3.1.8 Exact Recoverability of Robust LRR and Robust Latent LRR2.3.2 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; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis 4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering 4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; Single-Feature Case; Multiple-Feature Case; 5.7 Partial-Duplicate Image Search [54]; 5.7.1 Implementation Details; Modeling Global Geometric Consistency with a Low-Rank Matrix; Modeling False Matches with a Sparse Matrix Front Cover; Low-Rank Models in Visual Analysis; Copyright; Contents; About the Authors; Preface; Acknowledgment; Notations; 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; 2.3.1.1 Incoherence Conditions; 2.3.1.2 Exact Recoverability of MC; 2.3.1.3 Exact Recoverability of RPCA; 2.3.1.4 Exact Recoverability of RPCA with Missing Values; 2.3.1.5 Exact Recoverability of Outlier Pursuit; 2.3.1.6 Exact Recoverability of Outlier Pursuit with Missing Values; 2.3.1.7 Exact Recoverability of LRR Includes bibliographical references and index. - Vendor-supplied metadata |
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Gradient; 4.1.2 Frank-Wolfe Algorithm; 4.1.3 Alternating Direction Method; 4.1.3.1 Applying ADM to RPCA; 4.1.3.2 Experiments; 4.1.4 Linearized Alternating Direction Method with Adaptive Penalty; 4.1.4.1 Convergence Analysis</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">4.1.4.2 Applying LADMAP to LRR4.1.4.3 Experiments; 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; 4.2.2.1 Convergence Analysis; 4.2.3 Truncated Nuclear Norm Minimization; 4.2.4 Iteratively Reweighted Least Squares; 4.2.4.1 Convergence Analysis; 4.2.4.2 Experiments; 4.2.5 Factorization Method; 4.3 Randomized Algorithms; 4.3.1 l1 Filtering Algorithm; Recovery of a Seed Matrix; l1 Filtering</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">4.3.1.1 Complexity Analysis4.3.1.2 Experiments; 4.3.2 l2,1 Filtering Algorithm; Recovery of a Seed Matrix; l2,1 Filtering; 4.3.2.1 Theoretical Analysis; 4.3.2.2 Complexity Analysis; 4.3.2.3 Experiments; 4.3.3 Randomized Algorithm for Relaxed Robust LRR; 4.3.3.1 Complexity Analysis; 4.3.3.2 Experiments; 4.3.4 Randomized Algorithm for Online Matrix Completion; References; 5 Representative Applications; 5.1 Video Denoising [19]; 5.1.1 Implementation Details; Patch Matching with Outlier Removal; Denoising Patch Matrix; From Denoised Patch to Denoised Image/Video; 5.1.2 Experiments</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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]; Single-Feature Case; Multi-Feature Case; 5.6 Image Saliency Detection [21]; 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