A graph Laplacian based approach to semi-supervised feature selection for regression problems

Neurocomputing

Volumn 121, Issue , 2013, Pages 5-13

  Doquire, Gauthier ^a   Verleysen, Michel ^a  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

Feature selection; Graph Laplacian; Semi supervised learning

Indexed keywords

FEATURE SELECTION ALGORITHM; FEATURE SELECTION METHODS; GRAPH LAPLACIAN; MACHINE LEARNING APPLICATIONS; REAL-WORLD; REGRESSION PROBLEM; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING;

ALGORITHMS; FEATURE EXTRACTION; REGRESSION ANALYSIS; SUPERVISED LEARNING;

LAPLACE TRANSFORMS;

ALGORITHM; ANALYTIC METHOD; ARTICLE; CLASSIFICATION; GRAPH LAPLACIAN; MACHINE LEARNING; PRIORITY JOURNAL; PROBLEM SOLVING; REGRESSION ANALYSIS; SAMPLING; SEMI SUPERVISED FEATURE SELECTION;

EID: 84884139371     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.10.028     Document Type: Article

References (28)

1. Bellman R.E. Adaptive Control Processes-A Guided Tour 1961, Princeton University Press, Princeton, New Jersey, USA.
2. Verleysen M. Learning high-dimensional data. Limitations Future Trends Neural Comput. 2003, 186:141-162.
3. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
4. Guyon I., Gunn S., Nikravesh M., Zadeh L.A. Feature Extraction. Foundations and Applications 2006, Springer-Verlag New York, Inc.
5. Lee J.A., Verleysen M. Nonlinear Dimensionality Reduction 2007, Springer, New York, London.
6. Semi-Supervised Learning 2006, MIT Press, Cambridge, MA. O. Chapelle, B. Schölkopf, A. Zien (Eds.).
7. Zhu X., Goldberg A.B. Introduction to Semi-Supervised Learning 2009, Morgan & Claypool Publishers.
8. X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in: NIPS, vol. 17, 2005.
9. G. Doquire, M. Verleysen, Graph Laplacian for semi-supervised feature selection in regression problems, in: IWANN'11, Springer-Verlag, 2011, pp. 248-255.
10. Kohavi R., John G.H. Wrappers for feature subset selection. Artif. Intell. 1997, 97:273-324.
11. Battiti R. Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Networks 1994, 5:537-550.
12. Rossi F., Lendasse A., Francois D., Wertz V., Verleysen M. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemometrics Intell. Lab. Syst. 2006, 80:215-226.
13. M. Hall, Correlation-Based Feature Selection for Machine Learning, Ph.D. Thesis, University of Waikato, 1999.
14. Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 1996, 58(1):267-288.
15. Yuan M., Lin Y. Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc. Ser. B 2006, 68:49-67.
16. Mitra P., Member S., Murthy C.A., Pal S.K. Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24:301-312.
17. Dy J.G., Brodley C.E. Feature selection for unsupervised learning. J. Mach. Learn. Res. 2004, 5:845-889.
18. N.S. Madsen, C. Thomsen, J.M. Pena, Unsupervised feature subset selection, in: Proceedings of the Workshop on Probabilistic Graphical Models for Classification (PKDD'03), 2003, pp. 71-82.
19. Zhang D., Chen S., Zhou Z.-H. Constraint score. a new filter method for feature selection with pairwise constraints. Pattern Recognition 2008, 41:1440-1451.
20. Z. Zhao, H. Liu, Spectral feature selection for supervised and unsupervised learning, in: Proceedings of the 24th International Conference on Machine Learning, ICML '07, 2007, pp. 1151-1157.
21. I. Quinzán, J. M. Sotoca, F. Pla, Clustering-based feature selection in semi-supervised problems, in: ISDA, IEEE Computer Society, 2009, pp. 535-540.
22. Cai R., Zhang Z., Hao Z. Bassum. a Bayesian semi-supervised method for classification feature selection. Pattern Recognition 2011, 44(4):811-820.
23. Xu Z.L., King I., Lyu M.R.T., Jin R. Discriminative semi-supervised feature selection via manifold regularization. IEEE Trans. Neural Network. 2010, 21:1033-1047.
24. E. Zhong, S. Xie, W. Fan, J. Ren, J. Peng, K. Zhang, Graph-based iterative hybrid feature selection, in: Proceedings of the 2008 8th IEEE International Conference on Data Mining, ICDM '08, 2008, pp. 1133-1138.
25. Zhao J., Lu K., He X. Locality sensitive semi-supervised feature selection. Neurocomputing 2008, 71(10-12):1842-1849.
26. Chung F.R.K. Spectral Graph Theory 1997, American Mathematical Society.
27. Kraskov A., Stögbauer H., Grassberger P. Estimating mutual information. Phys. Rev. E. 2004, 69:066138. http://dx.doi.org/10.1103/PhysRevE.69.066138.
28. Rossi F., Delannay N., Conan-Guez B., Verleysen M. Representation of functional data in neural networks. Neurocomputing 2005, 64:183-210.