Time and space efficient spectral clustering via column sampling

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 2297-2304

  Li, Mu ^a   Lian, Xiao Chen ^a   Kwok, James T ^b   Lu, Bao Liang ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DIGITAL STORAGE; EIGENVALUES AND EIGENFUNCTIONS; IMAGE SEGMENTATION; PATTERN RECOGNITION;

DATA SET SIZE; EIGEN DECOMPOSITION; NUMBER OF DATUM; SINGLE- MACHINES; SPACE EFFICIENT; SPECTRAL CLUSTERING; SPECTRAL CLUSTERING ALGORITHMS; VERY LARGE DATUM;

CLUSTERING ALGORITHMS;

EID: 80052877369     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995425     Document Type: Conference Paper

References (18)

1. F. Bach and M. Jordan. Learning spectral clustering, with application to speech separation. Journal of Machine Learning Research, 7:1963-2001, 2006.
2. W. Chen, Y. Song, H. Bai, C. Lin, and E. Chang. Parallel spectral clustering in distributed systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.
3. D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603-619, 2002.
4. P. Drineas and M. Mahoney. On the Nyström method for approximating a Gram matrix for improved kernel-based learning. Journal of Machine Learning Research, 6:2153-2175, 2005.
5. F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19:355-369, March 2007.
6. C. Fowlkes, S. Belongie, F. Chung, and J. Malik. Spectral grouping using the Nyström method. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 214-225, 2004.
7. T. Kim and K. Lee. Learning full pairwise affinities for spectral segmentation. In CVPR, pages 2101-2108, 2010.
8. S. Kumar, M. Mohri, and A. Talwalkar. On sampling-based approximate spectral decomposition. In ICML, pages 585-592, 2009.
9. D. Lewis, Y. Yang, T. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5:361-397, 2004.
10. B. Long, Z. M. Zhang, X. Wú, and P. S. Yu. Spectral clustering for multi-type relational data. In ICML, pages 585-592, 2006.
11. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In NIPS, pages 849-856, 2001.
12. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2002.
13. A. Strehl and J. Ghosh. Cluster ensembles - A knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3:583-617, 2003.
14. M. Swain and D. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11-32, 1991.
15. A. Talwalkar, S. Kumar, and H. Rowley. Large-scale manifold learning. In CVPR, pages 1-8, 2008.
16. U. Von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17(4):395-416, 2007.
17. C. Williams and M. Seeger. Using the Nyström method to speed up kernel machines. In NIPS, 2001.
18. D. Yan, L. Huang, and M. Jordan. Fast approximate spectral clustering. In KDD, pages 907-916, 2009.