Abstract
We propose various novel embedded approaches for (simultaneous) feature selection and classification within a general optimisation framework. In particular, we include linear and nonlinear SVMs. We apply difference of convex functions programming to solve our problems and present results for artificial and real-world data.
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Ben-Tal, A., Zibulevsky, M.: Penalty/barrier multiplier methods for convex programming problems. SIAM Journal on Optimization 7(2), 347–366 (1997)
Bennett, K.P., Mangasarian, O.L.: Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software 1, 23–34 (1992)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
Bradley, P.S., Mangasarian, O.L.: Feature selection via concave minimization and support vector machines. In: Proceedings of the 15th International Conference on Machine Learning, San Francisco, CA, USA, pp. 82–90. Morgan Kaufmann, San Francisco (1998)
Cristianini, N., Shawe-Taylor, J., Elisseeff, A., Kandola, J.: On kernel-target alignment. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14, pp. 367–373. MIT Press, Cambridge (2002)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Pham Dinh, T., Hoai An, L.T.: A d.c. optimization algorithm for solving the trust-region subproblem. SIAM Journal on Optimization 8(2), 476–505 (1998)
Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton (1970)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.: Use of the zero-norm with linear models and kernel methods. Journal of Machine Learning Research 3, 1439–1461 (2003)
Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: Feature selection for SVMs. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 668–674. MIT Press, Cambridge (2001)
Zhu, J., Rosset, S., Hastie, T., Tibshirani, R.: 1-norm support vector machines. In: Thrun, S., Saul, L., Schölkopf, B. (eds.) Advances in Neural Information Processing Systems 16, MIT Press, Cambridge (2004)
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Neumann, J., Schnörr, C., Steidl, G. (2004). SVM-Based Feature Selection by Direct Objective Minimisation. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_26
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DOI: https://doi.org/10.1007/978-3-540-28649-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
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