Efficient greedy feature selection for unsupervised learning

Knowledge and Information Systems

Volumn 35, Issue 2, 2013, Pages 285-310

  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

DATA MINING; ERRORS; FEATURE EXTRACTION; GENETIC ALGORITHMS; UNSUPERVISED LEARNING;

DIMENSION REDUCTION TECHNIQUES; GREEDY ALGORITHMS; MACHINE LEARNING APPLICATIONS; RECONSTRUCTION ERROR; REDUNDANT FEATURES; RELEVANT FEATURES; STATE-OF-THE-ART METHODS; UNSUPERVISED FEATURE SELECTION;

LEARNING ALGORITHMS;

EID: 84876024189     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-012-0538-1     Document Type: Article

References (29)

1. Bolón-Canedo V, Sánchez-Maroño N, Alonso-Betanzos A (2012) A review of feature selection methods on synthetic data. Knowl Inf Syst 1-37. doi: 10. 1007/s10115-012-0487-8.
2. Boutsidis C, Mahoney M, Drineas P (2009) Unsupervised feature selection for the means clustering problem. In: Proceedings of advances in neural information processing systems (NIPS), vol 22. Curran Associates, Red Hook, pp 153-161.
3. Boutsidis C, Mahoney MW, Drineas P (2008) Unsupervised feature selection for principal components analysis. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD). ACM, New York, pp 61-69.
4. Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), ACM, New York, pp 333-342.
5. Cieri C, Graff D, Liberman M, Martey N, Strassel S (1999) The TDT-2 text and speech corpus. In: Proceedings of the DARPA Broadcast News, Workshop, pp 57-60.
6. Cole R, Fanty M (1990) Spoken letter recognition. In: Proceedings of the third DARPA workshop on speech and natural language, pp 385-390.
7. Cui Y, Dy J (2008) Orthogonal principal feature selection, the sparse optimization and variable selection workshop at the international conference on machine learning (ICML).
8. Dhillon I, Modha D (2001) Concept decompositions for large sparse text data using clustering. Mach Learn 42(1): 143-175.
9. Dhir C, Lee J, Lee S-Y (2012) Extraction of independent discriminant features for data with asymmetric distribution. Knowl Inf Syst 30: 359-375.
10. Farahat A, Ghodsi A, Kamel M (2011) An efficient greedy method for unsupervised feature selection. In: Proceedings of the 2011 IEEE 11th international conference on data mining (ICDM), pp 161-170.
11. Frey B, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814): 972.
12. Guyon I (2006) Feature extraction: foundations and applications. Springer, Berlin.
13. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3: 1157-1182.
14. He X, Cai D, Niyogi P (2006) Laplacian score for feature selection. In: Proceedings of advances in neural information processing systems (NIPS) 18, MIT Press, Cambridge, pp 507-514.
15. Hull J (1994) A database for handwritten text recognition research. IEEE Trans Pattern Anal Mach Intell 16(5): 550-554.
16. Jain A, Dubes R (1988) Algorithms for clustering data. Prentice-Hall, Upper Saddle River.
17. Jolliffe I (2002) Principal component analysis, 2nd edn. Springer, Berlin.
18. Lu Y, Cohen I, Zhou X, Tian Q (2007) Feature selection using principal feature analysis. In: Proceedings of the 15th international conference on multimedia. ACM, New York, pp 301-304.
19. Lütkepohl H (1996) Handbook of matrices. Wiley, New York.
20. Masaeli M, Yan Y, Cui Y, Fung G, Dy J (2010) Convex principal feature selection. In: Proceedings of SIAM international conference on data mining (SDM). SIAM, Philadelphia, pp 619-628.
21. Mitra P, Murthy C, Pal S (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3): 301-312.
22. Nene S, Nayar S, Murase H (1996) Columbia object image library (COIL-20), technical report CUCS-005-96, Columbia University.
23. Ng A, Jordan M, Weiss Y (2001) On spectral clustering: analysis and an algorithm. In: Proceedings of advances in neural information processing systems (NIPS), vol 14, MIT Press, Cambridge, pp 849-856.
24. Samaria F, Harter A (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the second IEEE workshop on applications of computer vision, pp 138-142.
25. Strehl A, Ghosh J (2003) Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3: 583-617.
26. Wolf L, Shashua A (2005) Feature selection for unsupervised and supervised inference: the emergence of sparsity in a weight-based approach. J Mach Learn Res 6: 1855-1887.
27. Zelnik-Manor L, Perona P (2004) Self-tuning spectral clustering. In: Proceedings of advances in neural information processing systems (NIPS), vol 16. MIT Press, Cambridge, pp 1601-1608.
28. Zhao Z, Liu H (2007) Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the 24th international conference on machine learning (ICML), ACM, New York, pp 1151-1157.
29. Zou H, Hastie T, Tibshirani R (2006) Sparse principal component analysis. J Comput Graph Stat 15(2): 265-286.