Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

IEEE Transactions on Knowledge and Data Engineering

Volumn 26, Issue 9, 2014, Pages 2138-2150

  Li, Zechao ^a   Liu, Jing ^b   Yang, Yi ^c   Zhou, Xiaofang ^b   Lu, Hanqing ^b  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b INSTITUTE OF AUTOMATION   (China)

^c UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

and association rules; classification; Clustering; Computing Methodologies; Database Applications; Database Management; Design Methodology; Feature evaluation and selection; Information Technology and Systems; Pattern Recognition

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; DATA MINING; FEATURE EXTRACTION; INFORMATION MANAGEMENT; ITERATIVE METHODS; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; STRUCTURAL ANALYSIS;

CLUSTERING; COMPUTING METHODOLOGIES; DATABASE APPLICATIONS; DATABASE MANAGEMENT; DESIGN METHODOLOGY; FEATURE EVALUATION AND SELECTION; INFORMATION TECHNOLOGY AND SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 84959493567     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2013.65     Document Type: Article

References (46)

1. A. Jain and D. Zongker, "Feature selection: Evaluation, application, and small sample performance," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 2, pp. 153-158, Feb. 1997.
2. R. Duda, P. Hart, and D. Stork, Pattern Recognition, 2nd ed. New York, NY, USA: Wiley, 2001.
3. H. Liu, X. Wu, and S. Zhang, "Feature selection using hierarchical feature clustering," in Proc. ACM Int. Conf. Inform. Knowl. Manage., New York, NY, USA, 2011.
4. 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, Nov. 2005.
5. G. Forman, "An extensive empirical study of feature selection metrics for text classification," J. Mach. Learn. Res., vol. 3, pp. 1289-1305, Mar. 2003.
6. M. Sikonja and I. Kononenko, "Theoretical and empirical analysis of relief and relieff," Mach. Learn., vol. 53, no. 1-2, pp. 23-69, 2003.
7. Z. Zhao and H. Liu, "Spectral feature selection for supervised and unsupervised learning," in Proc. Int. Conf. Mach. Learn., Corvallis, OR, USA, 2007.
8. 2,1-norms minimization," in Proc. Adv. NIPS, 2010.
9. Z. Zhao and H. Liu, "Semi-supervised feature selection via spectral analysis," in Proc. SDM, 2007.
10. Z. Xu, I. King, M. R.-T. Lyu, and R. Jin, "Discriminative semi-supervised feature selection via manifold regularization," IEEE Trans. Neural Netw., vol. 21, no. 7, pp. 1033-1047, 2010.
11. J. G. Dy and C. E. Brodley, "Feature selection for unsupervised learning," J. Mach. Learn. Res., vol. 5, pp. 845-889, Dec. 2004.
12. D. Cai, C. Zhang, and X. He, "Unsupervised feature selection for multi-cluster data," in Proc. ACM SIGKDD Int. Conf. KDD, Washington, DC, USA, 2010.
13. 2,1-norm regularized discriminative feature selection for unsupervised learning," in Proc. 22nd IJCAI, 2011.
14. H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491-502, Apr. 2005.
15. B. Krishnapuram, A. J. Hartemink, L. Carin, and M. A. T. Figueiredo, "A bayesian approach to joint feature selection and classifier design," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1105-1111, Sept. 2004.
16. J. Tang, S. Yan, R. Hong, and G.-J. Q. a nd Tat-Seng Chua, "Inferring semantic concepts from community-contributed images and noisy tags," in Proc. 17th ACM Int. Conf. MM, New York, NY, USA, 2009, pp. 223-232.
17. J. Liu, M. Li, Q. Liu, H. Lu, and S. Ma, "Image annotation via multi-graph learning," Pattern Recognit., vol. 42, no. 2, pp. 218-228, 2009.
18. Z. Li, J. Liu, X. Zhu, T. Liu, and H. Lu, "Image annotation using multi-correlation probabilistic matrix factorization," in Proc. ACM Int. Conf. MM, New York, NY, USA, 2010, pp. 1187-1190.
19. Q. Cheng, H. Zhou, and J. Cheng, "The Fisher-Markov selector: Fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 6, pp. 1217-1233, Jun. 2011.
20. P. Mitra, C. A. Murthy, and S. K. Pal, "Unsupervised feature selection using feature similarity," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 3, pp. 301-312, Mar. 2002.
21. X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," in Proc. Adv. NIPS, 2005.
22. Y. M. Cheung and H. Zeng, "Local kernel regression score for selecting features of high-dimensional data," IEEE Trans. Knowl. Data Eng., vol. 21, no. 12, pp. 1798-1802, Dec. 2009.
23. M. Sahami, "Using machine learning to improve information access," Ph.D. dissertation, Stanford University, Stanford, CA, USA, 1998.
24. H. C. Peng, F. H. Long, and C. Ding, "Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundanct," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005.
25. L. Zhang, C. Chen, J. Bu, and X. He, "A unified feature and instance selection framework using optimum experimental design," IEEE Trans. Image Process., vol. 21, no. 5, pp. 2379-2388, May 2012.
26. H. Zeng and Y.-M. Cheung, "Feature selection and kernel learning for local learning-based clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1532-1547, Aug. 2011.
27. M. H. Law, M. Figueired, and A. K. Jain, "Feature selection in mixture-based clustering," in Proc. Adv. NIPS, 2002.
28. C. Constantinopoulos, M. K. Titsia, and A. Likas, "Bayesian feature and model selection for gaussian mixture models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 6, pp. 1013-1018, Jun. 2006.
29. Z. Li, Y. Yang, J. Liu, X. Zhou, and H. Lu, "Unsupervised feature selection using nonnegative spectral analysis," in Proc. Conf. AAAI, 2012.
30. Y. Yang, H. T. Shen, F. Nie, R. Ji, and X. Zhou, "Nonnegative spectral clustering with discriminative regularization," in Proc. Nat. Conf. Artif. Intell., 2011.
31. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.
32. A. Y. Ng, M. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in Proc. Adv. NIPS, 2001.
33. R. K. Ando and T. Zhang, "A framework for learning predictive structures from multiple tasks and unlabeld data," J. Mach. Learn. Res., vol. 6, pp. 1817-1853, Nov. 2005.
34. S. Ji, L. Zhang, S. Yu, and J. Ye, "A shared-subspace learning framework for multi-label classification," ACM Trans. Knowl. Discov. Data, vol. 4, no. 2, pp. 1817-1853, 2010.
35. Z. Ma et al., "Web image annotation via subspace-sparsity collaborated feature selection," IEEE Trans. Multimedia, vol. 14, no. 4, pp. 1021-1030, Aug. 2012.
36. N. J. Higham, Accuracy and Stability of Numerical Algorithms, 2nd ed. Manchester, U.K.: Society for Industrial and Applied Mathematics, 2002.
37. H. Kuhn and A. Tucker, "Nonlinear programming," in Proc. Berkeley Symp. Math. Statist. Probab., 1951, pp. 481-492.
38. D. Lee and H. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, pp. 788-791, Oct. 1999.
39. Z. Zhao, L. Wang, H. Liu, and J. Ye, "On similarity preserving feature selection," IEEE Trans. Knowl. Data Eng., vol. 25, no. 3, pp. 619-632, Mar. 2013.
40. [Online]. Available: http://cs.nyu.edu/∼roweis/data.html
41. M. J. Lyons, J. Budynek, and S. Akamatsu, "Automatic classification of single facial images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 12, pp. 1357-1362, Dec. 1999.
42. N. Gourier, D. Hall, and J. Crowley, "Estimating face orientation from robust detection of salient facial features," in Proc. ICPR Workshop Visual Observation Deictic Gestures, 2004.
43. Y. Yang, D. Xu, F. N. S. Yan, and Y. Zhuang, "Image clustering using local discriminant models and global integration," IEEE Trans. Image Process., vol. 19, no. 10, pp. 2761-2773, Oct. 2010.
44. M. Craven et al. "Learning to extract symbolic knowledge from the world wide web," in Proc. Nat. Conf. Artif. Intell., 1998.
45. [Online]. Available: http://tunedit.org/repo/Data/Text-wc
46. [Online]. Available: http://featureselection.asu.edu/datasets.php