Active spectral clustering via iterative uncertainty reduction

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 1339-1347

  Wauthier, Fabian L ^a   Jojic, Nebojsa ^b   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

active learning; spectral clustering

Indexed keywords

ACTIVE LEARNING; ACTIVE-LEARNING ALGORITHM; CLUSTERING SOLUTIONS; DATA SETS; SIMILARITY MEASUREMENTS; SPECTRAL CLUSTERING; UNCERTAINTY REDUCTION;

DATA MINING; ESTIMATION; LEARNING ALGORITHMS;

UNCERTAINTY ANALYSIS;

EID: 84866038973     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339737     Document Type: Conference Paper

References (27)

1. D. Achlioptas and F. McSherry. Fast Computation of Low-Rank Matrix Approximations. Journal of the ACM, 54, 2007.
2. Z. Bodó, Z. Minier, and L. Csató. Active Learning with Clustering. Journal of Machine Learning Research, 16:127-139, 2011.
3. C. Campbell, N. Cristianini, and A. Smola. Query Learning with Large Margin Classifiers. In Proceedings of the International Conference on Machine Learning (ICML), pages 111-118. Morgan Kaufmann, 2000.
4. P. Drineas and R. Kannan. Pass-Efficient Algorithms for Approximating Large Matrices. In Proceedings of the Annual Symposium on Discrete Algorithms, pages 223-232, 2003.
5. P. Drineas, R. Kannan, and M. W. Mahoney. Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix. SIAM Journal on Computing, 36:158-183, 2006.
6. P. Drineas and M. W. Mahoney. On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. Journal of Machine Learning Research, 6:2153-2175, 2005.
7. C. Fowlkes, S. Belongie, F. Chung, and J. Malik. Spectral Grouping using the Nyström Method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 2004.
8. A. Frank and A. Asuncion. UCI Machine Learning Repository, 2010.
9. A. Frieze, R. Kannan, and S. Vempala. Fast Monte-Carlo Algorithms for Finding Low-Rank Approximations. Journal of the ACM, 51:1025-1041, 2004.
10. T. Gärtner. A Survey of Kernels for Structured Data. SIGKDD Explorations, 2003.
11. L. Huang, D. Yan, M. I. Jordan, and N. Taft. Spectral Clustering with Perturbed Data. In Advances in Neural Information Processing Systems 21 (NIPS), pages 705-712, 2009.
12. N. Jojic, A. Perina, and V. Murino. Structural Epitome: A Way to Summarize One's Visual Experience. In Advances in Neural Information Processing Systems 23 (NIPS), pages 1027-1035. 2011.
13. P. K. Mallapragada, R. Jin, and A. K. Jain. Active Query Selection for Semi-Supervised Clustering. In ICPR, pages 1-4. IEEE, 2008.
14. A. Y. Ng, M. I. Jordan, and Y. Weiss. On Spectral Clustering: Analysis and an Algorithm. In Advances in Neural Information Processing Systems 14 (NIPS), pages 849-856. MIT Press, 2002.
15. University of Washington Information Design Lab. [idl.ee.washington.edu/ SimilarityLearning/Applications/Datasets/]. Face Similarity Data, 2012.
16. A. Rahimi and B. Recht. Clustering with Normalized Cuts is Clustering with a Hyperplane. In Statistical Learning in Computer Vision, 2004.
17. T. Sakai and A. Imiya. Fast Spectral Clustering with Random Projection and Sampling. In Proceedings of the International Conference on Machine Learning and Data Mining in Pattern Recognition, pages 372-384, Berlin, Heidelberg, 2009. Springer-Verlag.
18. O. Shamir and N. Tishby. Spectral Clustering on a Budget. Proceedings of the Infernational Conference on Artificial Intelligence and Statistics (AISTATS), 2011.
19. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, New York, NY, USA, 2004.
20. J. Shi and J. Malik. Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000.
21. G. W. Stewart and J. Sun. Matrix Perturbation Theory. Computer Science and Scientific Computing. Academic Press, 1990.
22. G. Takács, I. Pilászy, B. Németh, and D. Tikk. Investigation of Various Matrix Factorization Methods for Large Recommender Systems. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, pages 6:1-6:8, New York, NY, USA, 2008. ACM.
23. S. Tong and D. Koller. Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2:45-66, 2002.
24. U. von Luxburg. A Tutorial on Spectral Clustering. Statistics and Computing, 17:395-416, 2007.
25. X. Wang and I. Davidson. Active Spectral Clustering. In IEEE International Conference on Data Mining, pages 561-568, 2010.
26. C. K. I. Williams and M. Seeger. Using the Nyström Method to Speed Up Kernel Machines. In Advances in Neural Information Processing Systems 13 (NIPS), pages 682-688. MIT Press, 2001.
27. Q. Xu, M. desJardins, and K. L. Wagstaff. Active Constrained Clustering by Examining Spectral Eigenvectors. In Proceedings of the International Conference on Discovery Science, pages 294-307, 2005.