Constrained Laplacian score for semi-supervised feature selection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6911 LNAI, Issue PART 1, 2011, Pages 204-218

  Benabdeslem, Khalid ^a   Hindawi, Mohammed ^a  

^a UNIVERSITÉ LYON 1   (France)

Author keywords

Constraints; Feature selection; Laplacian score

Indexed keywords

CONSTRAINTS; FEATURE SELECTION METHODS; FILTER-BASED; HIGH DIMENSIONAL DATA; LABELED DATA; LAPLACIANS; SEMI-SUPERVISED; SPECTRAL GRAPH THEORY; UNLABELED DATA; LOCALITY-PRESERVING;

ALGORITHMS; DATA REDUCTION; GRAPH THEORY; LAPLACE TRANSFORMS; LEARNING SYSTEMS; CLUSTERING ALGORITHMS; DATA MINING; GRAPH ALGORITHMS; SUPERVISED LEARNING;

FEATURE EXTRACTION;

EID: 80052407690     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23780-5_23     Document Type: Conference Paper

References (27)

1. Jain, A., Zongker, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(2), 153-158 (1997)
2. Frank, A., Asuncion, A.: Uci machine learning repository. Technical report, University of California (2010)
3. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research (3), 1157-1182 (2003)
4. Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlationbased filter solution. In: Proceedings of the Twentieth International Conference on Machine Learning (2003)
5. Kohavi, R., John, G.: Wrappers for feature subset selection. Artificial Intelligence 97(12), 273-324 (1997)
6. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by local linear embedding. Science (290), 2323-2326 (2000)
7. Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1(3), 131-156 (2000)
8. Dy, J., Brodley., C.E.: Feature selection for unsupervised learning. Journal of Machine Learning Research (5), 845-889 (2004)
9. Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning. The MIT Press, Cambridge (2006)
10. Zhao, Z., Liu, H.: Semi-supervised feature selection via spectral analysis. In: Proceedings of SIAM International Conference on Data Mining (SDM), pp. 641-646 (2007)
11. Chung, F.: Spectral graph theory. AMS, Providence (1997)
12. Ren, J., Qiu, Z., Fan, W., Cheng, H., Yu, P.S.: Forward semi-supervised feature selection. In:Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 970-976. Springer, Heidelberg (2008)
13. Basu, S., Davidson, I., Wagstaff, K.: Constrained clustering: Advances in algorithms, theory and applications. Chapman and Hall/CRC Data Mining and Knowledge Discovery Series (2008)
14. Xing, E., Ng, A., Jordan, M., Russel, S.: Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems, vol. 15, pp. 505-512 (2003)
15. Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research 6, 937-965 (2005)
16. Zhang, D., Zhou, Z., Chen, S.: Semi-supervised dimensionality reduction. In: Proceedings of SIAM International Conference on Data Mining, SDM (2007)
17. Zhang, D., Chen, S., Zhou, Z.: Constraint score: A new filter method for feature selection with pairwise constraints. Pattern Recognition 41(5), 1440-1451 (2008)
18. Sun, D., Zhan, D.: Bagging constraint score for feature selection with pairwise constraints. Pattern Recognition 43(6), 2106-2118 (2010)
19. Kalakech, M., Biela, P., Macaire, L., Hamad, D.: Constraint scores for semisupervised feature selection: A comparative study. Pattern Recognition Letters 32(5), 656-665 (2011)
20. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, vol. 17 (2005)
21. Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of relief and relieff. Machine Learning 53, 23-69 (2003)
22. Zhao, Z., Liu, H.: Spectral feature selection for supervised and unsupervised learning. In: Proceedings of the Twenty Fourth International Conference on Machine Learning (2007)
23. Kohonen, T.: Self Organizing Map. Springer, Berlin (2001)
24. Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, L., Caligiuri, M., Bloomfield, C., Lander, E.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 15 286(5439), 531-537 (1999)
25. Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Natl. Acad. Sci. 96(12), 6745-6750 (1999)
26. Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
27. Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, Hoboken (2000)