An efficient greedy method for unsupervised feature selection

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 161-170

  Farahat, Ahmed K ^a   Ghodsi, Ali ^a   Kamel, Mohamed S ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Feature selection; Greedy algorithms; Unsupervised learning

Indexed keywords

CLASS LABELS; DATA MATRICES; DATA MINING APPLICATIONS; GREEDY ALGORITHMS; GREEDY METHOD; LEARNING TASKS; NOVEL ALGORITHM; REAL DATA SETS; RECONSTRUCTION ERROR; RECURSIVE FORMULA; STATE-OF-THE-ART METHODS; UNSUPERVISED FEATURE SELECTION;

DATA MINING; LEARNING ALGORITHMS; UNSUPERVISED LEARNING;

FEATURE EXTRACTION;

EID: 84857146334     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.22     Document Type: Conference Paper

References (17)

1. I. Jolliffe, Principal Component Analysis, 2nd ed. Springer, 2002.
2. H. Zou, T. Hastie, and R. Tibshirani, "Sparse principal component analysis," J. Comput. Graph. Stat., vol. 15, no. 2, pp. 265-286, 2006.
3. M. Masaeli, Y. Yan, Y. Cui, G. Fung, and J. Dy, "Convex principal feature selection," in Proceedings of SIAM International Conference on Data Mining (SDM), 2010, pp. 619-628.
4. Y. Cui and J. Dy, "Orthogonal principal feature selection," in the Sparse Optimization and Variable Selection Workshop at the International Conference on Machine Learning (ICML), 2008.
5. Y. Lu, I. Cohen, X. Zhou, and Q. Tian, "Feature selection using principal feature analysis," in Proceedings of the 15th International Conference on Multimedia. New York, NY, USA: ACM, 2007, pp. 301-304.
6. C. Boutsidis, M. W. Mahoney, and P. Drineas, "Unsupervised feature selection for principal components analysis," in Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08). New York, NY, USA: ACM, 2008, pp. 61-69.
7. C. Boutsidis, M. Mahoney, and P. Drineas, "Unsupervised feature selection for the k-means clustering problem," in Advances in Neural Information Processing Systems 22, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, Eds., 2009, pp. 153-161.
8. X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," in Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf, and J. Platt, Eds. Cambridge, MA, USA: MIT Press, 2006, pp. 507-514.
9. Z. Zhao and H. Liu, "Spectral feature selection for supervised and unsupervised learning," in Proceedings of the 24th International Conference on Machine learning. New York, NY, USA: ACM, 2007, pp. 1151-1157.
10. L. Wolf and A. Shashua, "Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach," J. Mach. Learn. Res., vol. 6, pp. 1855-1887, 2005.
11. D. Cai, C. Zhang, and X. He, "Unsupervised feature selection for multi-cluster data," in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10). New York, NY, USA: ACM, 2010, pp. 333-342.
12. A. Ng, M. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in Advances in Neural Information Processing Systems 14 (NIPS'01). Cambridge, MA, USA: MIT Press, 2001, pp. 849-856.
13. P. Mitra, C. Murthy, and S. Pal, "Unsupervised feature selection using feature similarity," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 301-312, 2002.
14. H. Lütkepohl, Handbook of Matrices. John Wiley & Sons Inc, 1996.
15. A. Jain and R. Dubes, Algorithms for Clustering Data. Prentice-Hall, Inc., 1988.
16. B. Frey and D. Dueck, "Clustering by passing messages between data points," Science, vol. 315, no. 5814, p. 972, 2007.
17. A. Strehl and J. Ghosh, "Cluster ensembles-a knowledge reuse framework for combining multiple partitions," J. Mach. Learn. Res., vol. 3, pp. 583-617, 2003.