Abstract
Sparse representation based classification (SRC) has been very successful in many pattern recognition problems. Recently, some extended kernel methods have been proposed through mapping the samples from original feature space into a high dimensional feature space, and then performing the SRC in the high dimensional feature space. However they are all simple kernel methods whose kernel is not most suitable one. For addressing this question, we proposed a novel method named multiple kernel sparse representation based classification (MKSRC), which combine several possible kernels and make full of kernel information. More importantly kernel weights of MKSRC can be automatically selected. The experimental results of face databases indicated recognition performance of new method is superior to other state-of-the-art methods.
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References
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Hart, P.E.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theory 16, 515–516 (1968)
Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. Syst. Man Cybern. SMC-2, 408–421 (1972)
Aizerman, M.A., Braverman, E.M., Rozonoer, L.I.: T heoretical foundation of potential function method in pattern recognition learning. Automat. Remote Contr. 25, 821–837 (1964)
Scholkopf, B., Smola, A., Muller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1998)
Mike, S., Ratsch, G., Weston, J., Scholkopf, B., Muller, K.R.: Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing, vol. IX, pp. 41–48 (1999)
Mike, S., Ratsch, G., Scholkopf, B., Smola, A., Weston, J., Muller, K.R.: Invariant feature extraction and classification in kernel spaces. In: Proceedings of the 13th Annual Neural Information Processing Systems Conference, pp. 526–532 (1999)
Argyriou, A., Hauser, R., Micchelli, C.A., Pontil, M.: A DC algorithm for kernel selection. In: Proc. 23rd Int. Conf. Mach., Pittsburgh, PA, pp. 41–49 (2006)
Argyriou, A., Micchelli, C.A., Pontil, M.: Learning convex combinations of continuously parameterized basic kernels. In: Proc. 18th Annu. Conf. Learn. Theory, Bertinoro, Italy, pp. 338–352 (2005)
Ong, C.S., Smola, A.J., Williamson, R.C.: Learning the kernel with hyperkernels. J. Mach. Learn. Res. 6, 1043–1071 (2005)
Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: More efficiency in multiple kernel learning. In: Proc. 24th Int. Conf. Mach. Learn., Corvallis, OR, pp. 775–782 (2007)
Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: Grandvalet: Simple MKL. J. Mach. Learn. Res. 9, 2491–2521 (2008)
Sonnenburg, S., Ratsch, G., Schafer, C., Scholkopf, B.: Large scale multiple kernel earning. J. Mach. Learn. Res. 7, 1531–1565 (2006)
Zien, A., Ong, C.S.: Multiclass multiple kernel learning. In: Proc. 24th Int. Conf. Mach. Learn., Corvallis, OR, pp. 1191–1198 (2007)
Burges, C.J.C.: Simplified support vector decision rules. In: Proc.13th Int. Conf. Mach. Learn., San Mateo, CA, pp. 71–77 (1996)
Scholkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Nguyen, D., Ho, T.: An efficient method for simplifying support vector machines. In: Proc. 22nd Int. Conf. Mach. Learn., Bonn, Germany, pp. 617–624 (2005)
Wu, M., Scholkopf, B., Bakir, B.: A direct method for building sparse kernel learning algorithms. J. Mach. Learn. Res. 7, 603–624 (2006)
Wu, M., Scholkopf, B., Bakir, G.: Building sparse large margin classifiers. In: Proc. 22nd Int. Conf. Mach. Learn., Bonn, Germany, pp. 996–1003 (2005)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. TPAMI 31(2), 210–227 (2009)
Yin, J., Jin, Z.: Kernel sparse representation based classification. Neurocomputing 77(1), 120–128 (2012)
Gao, S., Tsang, I.W.-H., Chia, L.-T.: Kernel Sparse Representation for Image Classification and Face Recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 1–14. Springer, Heidelberg (2010)
Zhang, L., Zhou, W.-D.: Kernel sparse representation-based classifier. IEEE Transactions on Signal Processing 60(4), 1684–1695 (2012)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B 58(1), 267–288 (1996)
Lanckriet, G.R.G., et al.: Learning the Kernel Matrix with Semidefinite Programming. J. Machine Learning Research 5, 27–72 (2004)
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Zheng, H., Liu, F., Jin, Z. (2012). Multiple Kernel Sparse Representation Based Classification. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_7
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DOI: https://doi.org/10.1007/978-3-642-33506-8_7
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