Efficient subspace segmentation via quadratic programming

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2011, Pages 519-524

  Wang, Shusen ^a   Yuan, Xiaotong ^b   Yao, Tiansheng ^a   Yan, Shuicheng ^b   Shen, Jialie ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c SINGAPORE MANAGEMENT UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

AFFINE SUBSPACES; AFFINITY MATRIX; ALGORITHMIC SOLUTIONS; COEFFICIENT MATRIX; DATA NOISE; HOPKINS; LINEAR COMBINATIONS; MULTIPLE CLUSTERS; QUADRATIC PROGRAMMING PROBLEMS; SEGMENTATION ACCURACY; SEGMENTATION RESULTS; SPECTRAL CLUSTERING; SUB-SPACE SEGMENTATION; SUBSPACE CLUSTERING; TOY DATA; YALE FACE DATABASE;

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; EXPERIMENTS; OPTIMIZATION;

VECTORS;

EID: 80055057840     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Agarwal, P. K., and Mustafa, N. H. 2004. k-means projective clustering. In Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 155-165.
2. Birgin, E. G.; Martínez, J. M.; and Raydan, M. 1999. Non-monotone spectral projected gradient methods on convex sets. SIAM Journal on Optimization 10:1196-1211.
3. Boult, T., and Brown, L. 1991. Factorization-based segmentation of motions. In Proceedings of the IEEE Workshop on Visual Motion, 179-186.
4. Costeira, J., and Kanade, T. 1998. A multibody factorization method for independently moving objects. International Journal of Computer Vision 19(3):159-179. (Pubitemid 128517909)
5. Elhamifar, E., and Vidal, R. 2009. Sparse subspace clustering. In IEEE Conference on Computer Vision and Pattern Recognition.
6. Fischler, M. A., and Bolles, R. C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of ACM 24:381-395.
7. Georghiades, A.; Belhumeur, P.; and Kriegman, D. 2001. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Conference on Computer Vision and Pattern Recognition 23(6):643-660.
8. Ho, J.; Yang, M.-H.; Lim, J.; Lee, K.-C.; and Kriegman, D. 2003. Clustering appearances of objects under varying illumination conditions. In IEEE Conference on Computer Vision and Pattern Recognition.
9. Hong, W.; Wright, J.; Huang, K.; and Ma, Y. 2006. Multi-scale hybrid linear models for lossy image representation. IEEE Transactions on Image Processing 15(12):3655-3671. (Pubitemid 44811680)
10. Lee, K.; Ho, J.; and Kriegman, D. 2005. Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5):684-698. (Pubitemid 40608402)
11. Lin, Z.; Chen, M.; Wu, L.; and Ma, Y. 2009. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. UIUC Technical Report, UILU-ENG-09-2215.
12. Liu, G.; Lin, Z.; and Yu, Y. 2010. Robust subspace segmentation by low-rank representation. In Proceedings of the 27th International Conference on Machine Learning.
13. Ma, Y.; Derksen, H.; Hong, W.; and Wright, J. 2007. Segmentation of multivariate mixed data via lossy data coding and compression. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(9):1546-1562. (Pubitemid 47299910)
14. Rao, S.; Tron, R.; Ma, Y.; and Vidal, R. 2008. Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories. In IEEE Conference on Computer Vision and Pattern Recognition.
15. Tron, R., and Vidal, R. 2007. A benchmark for the comparison of 3-d motion segmentation algorithms. In IEEE Conference on Computer Vision and Pattern Recognition.
16. Vidal, R., and Hartley, R. 2004. Motion segmentation with missing data using power factorization and gpca. In IEEE Conference on Computer Vision and Pattern Recognition.
17. Vidal, R.; Ma, Y.; and Sastry, S. 2005. Generalized principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(12):1-15.
18. Vidal, R. 2010. A tutorial on subspace clustering. To appear in the IEEE Signal Processing Magazine.