Effective global approaches for mutual information based feature selection

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

Volumn , Issue , 2014, Pages 512-521

  Nguyen, Xuan Vinh ^a   Chan, Jeffrey ^a   Romano, Simone ^a   Bailey, James ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

feature selection; global optimization; mutual information; semi definite programming; spectral relaxation

Indexed keywords

EID: 84907020153     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623611     Document Type: Conference Paper

References (27)

1. K. Bache and M. Lichman. UCI machine learning repository, 2013.
2. R. Battiti. Using mutual information for selecting features in supervised neural net learning. Neural Networks, IEEE Transactions on, 5(4):537-550, 1994.
3. G. Brown, A. Pocock, M.-J. Zhao, and M. Luján. Conditional likelihood maximisation: A unifying framework for information theoretic feature selection. J. Mach. Learn. Res., 13:27-66, Mar. 2012.
4. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
5. A. W. Chaovalitwongse, I. P. Androulakis, and P. M. Pardalos. Quadratic integer programming: complexity and equivalent forms quadratic integer programming: Complexity and equivalent forms. In C. A. Floudas and P. M. Pardalos, editors, Encyclopedia of Optimization, pages 3153-3159. 2009.
6. H. Cheng, Z. Qin, W. Qian, and W. Liu. Conditional mutual information based feature selection. In Knowledge Acquisition and Modeling, pages 103-107, 2008.
7. C. Ding and H. Peng. Minimum redundancy feature selection from microarray gene expression data. In Bioinformatics Conference, 2003, pages 523-528, 2003.
8. U. M. Fayyad and K. B. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In IJCAI, pages 1022-1029, 1993.
9. C. Fowlkes, S. Belongie, and J. Malik. Efficient spatiotemporal grouping using the nystrom method. In CVPR 2001, volume 1, pages I-231-I-238 vol.1, 2001.
10. J. Gallier. Geometric Methods and Applications: For Computer Science and Engineering. Texts in Applied Mathematics. Springer-Verlag GmbH, 2001.
11. M. X. Goemans and D. P. Williamson. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM, 42(6):1115-1145, Nov. 1995.
12. M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming, version 2.0 beta. http://cvxr.com/cvx, Sept. 2013.
13. B. Guo and M. Nixon. Gait feature subset selection by mutual information. IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, 39(1):36-46, 2009.
14. G. Herman, B. Zhang, Y. Wang, G. Ye, and F. Chen. Mutual information-based method for selecting informative feature sets. Pattern Recognition, 46(12):3315-3327, 2013.
15. M. Li, J. T. Kwok, and B.-L. Lu. Making large-scale nyström approximation possible. In J. Fürnkranz and T. Joachims, editors, ICML, pages 631-638. Omnipress, 2010.
16. D. Lin and X. Tang. Conditional infomax learning: an integrated framework for feature extraction and fusion. In ECCV'06.
17. C.-L. Liu, F. Yin, D.-H. Wang, and Q.-F. Wang. Casia online and offline chinese handwriting databases. In Document Analysis and Recognition (ICDAR), 2011 International Conference on, pages 37-41, Sept 2011.
18. Z.-Q. Luo, W.-K. Ma, A.-C. So, Y. Ye, and S. Zhang. Semidefinite relaxation of quadratic optimization problems. Signal Processing Magazine, IEEE, 27(3):20-34, 2010.
19. T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka. Distance-based image classification: Generalizing to new classes at near-zero cost. IEEE TPAMI, 35(11):2624-2637, Nov 2013.
20. H. Peng, F. Long, and C. Ding. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 27(8):1226-1238, 2005.
21. I. Rodriguez-Lujan, R. Huerta, C. Elkan, and C. S. Cruz. Quadratic programming feature selection. J. Mach. Learn. Res., 11:1491-1516, 2010.
22. J. M. Sotoca and F. Pla. Supervised feature selection by clustering using conditional mutual information-based distances. Pattern Recognition, 43(6):2068-2081, June 2010.
23. K. C. Toh, M. Todd, and R. H. Tütüncü. Sdpt3 - a matlab software package for semidefinite programming. Optimization methods and software, 11:545-581, 1999.
24. N. Vinh and J. Bailey. Comments on supervised feature selection by clustering using conditional mutual information-based distances. Pattern Recognition, 46(4):1220-1225, 2013.
25. D. H. Wolpert. The lack of a priori distinctions between learning algorithms. Neural computation, 8(7):1341-1390, 1996.
26. M. Yamashita, K. Fujisawa, M. Fukuda, K. Nakata, and M. Nakata. Algorithm 925: Parallel solver for semidefinite programming problem having sparse schur complement matrix. ACM Trans. Math. Softw., 39(1):6:1-6:22, Nov. 2012.
27. Y. Zhang, S. Burer, and W. N. Street. Ensemble pruning via semi-definite programming. J. Mach. Learn. Res., 7:1315-1338, Dec. 2006.