390 related articles for article (PubMed ID: 28531874)
1. Sparse subspace clustering for data with missing entries and high-rank matrix completion.
Fan J; Chow TWS
Neural Netw; 2017 Sep; 93():36-44. PubMed ID: 28531874
[TBL] [Abstract][Full Text] [Related]
2. Sparse subspace clustering: algorithm, theory, and applications.
Elhamifar E; Vidal R
IEEE Trans Pattern Anal Mach Intell; 2013 Nov; 35(11):2765-81. PubMed ID: 24051734
[TBL] [Abstract][Full Text] [Related]
3. Robust recovery of subspace structures by low-rank representation.
Liu G; Lin Z; Yan S; Sun J; Yu Y; Ma Y
IEEE Trans Pattern Anal Mach Intell; 2013 Jan; 35(1):171-84. PubMed ID: 22487984
[TBL] [Abstract][Full Text] [Related]
4. Accelerated low-rank representation for subspace clustering and semi-supervised classification on large-scale data.
Fan J; Tian Z; Zhao M; Chow TWS
Neural Netw; 2018 Apr; 100():39-48. PubMed ID: 29475014
[TBL] [Abstract][Full Text] [Related]
5. Learning Markov Random Walks for robust subspace clustering and estimation.
Liu R; Lin Z; Su Z
Neural Netw; 2014 Nov; 59():1-15. PubMed ID: 25005156
[TBL] [Abstract][Full Text] [Related]
6. Similarity preserving low-rank representation for enhanced data representation and effective subspace learning.
Zhang Z; Yan S; Zhao M
Neural Netw; 2014 May; 53():81-94. PubMed ID: 24561453
[TBL] [Abstract][Full Text] [Related]
7. Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint.
Lu GF; Yu QR; Wang Y; Tang G
Neural Netw; 2020 May; 125():214-223. PubMed ID: 32146353
[TBL] [Abstract][Full Text] [Related]
8. Adaptive low-rank subspace learning with online optimization for robust visual tracking.
Liu R; Wang D; Han Y; Fan X; Luo Z
Neural Netw; 2017 Apr; 88():90-104. PubMed ID: 28222299
[TBL] [Abstract][Full Text] [Related]
9. Robust auto-weighted multi-view subspace clustering with common subspace representation matrix.
Zhuge W; Hou C; Jiao Y; Yue J; Tao H; Yi D
PLoS One; 2017; 12(5):e0176769. PubMed ID: 28542234
[TBL] [Abstract][Full Text] [Related]
10. Recovering the missing components in a large noisy low-rank matrix: application to SFM.
Chen P; Suter D
IEEE Trans Pattern Anal Mach Intell; 2004 Aug; 26(8):1051-63. PubMed ID: 15641734
[TBL] [Abstract][Full Text] [Related]
11. The augmented lagrange multipliers method for matrix completion from corrupted samplings with application to mixed Gaussian-impulse noise removal.
Meng F; Yang X; Zhou C
PLoS One; 2014; 9(9):e108125. PubMed ID: 25248103
[TBL] [Abstract][Full Text] [Related]
12. Cancer molecular pattern discovery by subspace consensus kernel classification.
Han X
Comput Syst Bioinformatics Conf; 2007; 6():55-65. PubMed ID: 17951812
[TBL] [Abstract][Full Text] [Related]
13. Low-rank matrix fitting based on subspace perturbation analysis with applications to structure from motion.
Jia H; Martinez AM
IEEE Trans Pattern Anal Mach Intell; 2009 May; 31(5):841-54. PubMed ID: 19299859
[TBL] [Abstract][Full Text] [Related]
14. Affine Subspace Robust Low-Rank Self-Representation: From Matrix to Tensor.
Tang Y; Xie Y; Zhang W
IEEE Trans Pattern Anal Mach Intell; 2023 Aug; 45(8):9357-9373. PubMed ID: 37028386
[TBL] [Abstract][Full Text] [Related]
15. LogDet Rank Minimization with Application to Subspace Clustering.
Kang Z; Peng C; Cheng J; Cheng Q
Comput Intell Neurosci; 2015; 2015():824289. PubMed ID: 26229527
[TBL] [Abstract][Full Text] [Related]
16. Constrained Low-Rank Representation for Robust Subspace Clustering.
Wang J; Wang X; Tian F; Liu CH; Yu H
IEEE Trans Cybern; 2017 Dec; 47(12):4534-4546. PubMed ID: 27831896
[TBL] [Abstract][Full Text] [Related]
17. Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework.
Chun-Guang Li ; Chong You ; Vidal R
IEEE Trans Image Process; 2017 Jun; 26(6):2988-3001. PubMed ID: 28410106
[TBL] [Abstract][Full Text] [Related]
18. Tensor completion for estimating missing values in visual data.
Liu J; Musialski P; Wonka P; Ye J
IEEE Trans Pattern Anal Mach Intell; 2013 Jan; 35(1):208-20. PubMed ID: 22271823
[TBL] [Abstract][Full Text] [Related]
19. Traffic speed data imputation method based on tensor completion.
Ran B; Tan H; Feng J; Liu Y; Wang W
Comput Intell Neurosci; 2015; 2015():364089. PubMed ID: 25866501
[TBL] [Abstract][Full Text] [Related]
20. Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation.
Fang X; Xu Y; Li X; Lai Z; Wong WK
IEEE Trans Cybern; 2016 Aug; 46(8):1828-38. PubMed ID: 26259210
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]