Regularized Jacobi Wavelets Kernel for Support Vector Machines

  • Abbassa Nadira University of Abdelhamid Ibn Badis Mostaganem
  • Amir Abdessamad University of Abdelhamid Ibn Badis Mostaganem
  • Bahri Sidi Mohamed University of Abdelhamid Ibn Badis Mostaganem
Keywords: SVM, Jacobi polynomials, Jacobi wavelets, Kernel, Reproducing Kernel Hilbert Space, Frame.

Abstract

A new family of regularized Jacobi wavelets is constructed. Based on this Jacobi wavelets, a new kernel for support vector machines is presented. Using kernel and frame theory, the Reproducing Kernel Hilbert Space of this kernel is identified. We show that without being a universal kernel, the proposed one possesses a good separation property and a big ability to extract more discriminative features. These theoretical results are confirmed and supported by numerical experiments.

Author Biographies

Abbassa Nadira, University of Abdelhamid Ibn Badis Mostaganem
PhD student at the University of Mostaganem since 2015
Amir Abdessamad, University of Abdelhamid Ibn Badis Mostaganem
professor at the University of Mostaganem
Bahri Sidi Mohamed, University of Abdelhamid Ibn Badis Mostaganem
professor at the University of Mostaganem

References

M. Achache A weighted full- Newton step primal-dual interior point algorithm for convex quadratic optimization, Statistics, Optimization and Information Computing, vol. 2, pp. 21–32, 2014.

A. Bokhari, A. Amir, S. M. Bahri, A numerical approach to solve quadratic calculus of variation problems, Dynamics of Continuous, Discrete and Impulsive Systems Series B: Applications & Algorithms 25, pp. 427–440, 2018.

C. Burges A tutorial on support vector machines for pattern recognition, In Data Mining and Knowledge Discovery, Kluwer Academic Publishers, Boston, Vol. 2, 1998.

O. Christensen, An introduction to frames and Riesz bases, Applied and numerical harmonic analysis, Springer Science Business Media, LLC, 2002.

N. Cristianni, J. Shawe-Taylor, An introduction to Support Vector Machines and other kernel-based learning Methods, Cambridge University Press, 2000.

N. Djelloul, A. Amir, Analysis of Legendre Polynomial Kernel in Support Vector Machines, Int. J. Computing Science and Mathematics. To appear (2018).

H. Elaydi, A. A. Abu Haya, Solving Optimal Control Problem for Linear Time invariant Systems via Chebyshev Wavelet, International Journal of Electrical Engineering, Vol. 5, no. 5, pp. 541–556, 2012.

Y. Fillali, M. A. Sabri, A. Aarab An Improved Segmentation Approch for skin Lesion classification, Statistics, Optimization and Information Computing, vol. 7, pp. 456–467, 2019.

V. H. Moghaddam, J. Hamidzadel, New Hermite orthogonal polynomial kernel and combined kernels in support vector machine classifier, Pattern Recognition 60, Elsevier, pp. 921–935, 2016.

S. Ozer, C. H. Chen and H. A. Cirpan, A set of new Chebyshev kernel functions for support vector machine pattern classification, Pattern Recognition 44, pp. 1435–1447, Elsevier, 2011.

Z. B. Pan, H. Chan and X. H. You, Support vector machine with orthogonal Legendre kernel, Proceeding of the 2012 International Conference on Wavelet Analysis and Pattern Recognition, Xian, IEEE, 15-17 July 2012.

D. Peijun, T. Kun, X. Xiaoshi, Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification, Optics Communications 283, pp. 4978–4984, 2010.

A. Rakotomamonjy, S. Canu, Frames, Reproducing Kernels, Regularization, and Learning, Journal of Machine Learning Research 6, pp. 1485–1515, 2005.

M. Razzaghi, S. Yousefi, Legendre wavelets method for constrained optimal control problems, Math. Meth. Appl. Sci. 25, pp. 529–539, 2002.

R.A. Ryan, Introduction to Tensor Products of Banach Spaces, Monographs in Mathematics, Springer, London, 2002.

Spiliopoulou, M. Kruse, R. Borgelt, C. N¨urnberger, A. and W. Gaul (Eds.), From Data and Information Analysis to Knowledge Engineering, Proceedings of the 29th Annual Conference of the Gesellschaft f¨ur Klassifikation e.V., University of Magdeburg,Springer- Verlag, 2005.

I. Steinwart, A. Christmann, Support vector machines,Information Science and Statistics, Springer, 2008.

S. Suvrit, S. Nowozin, and S.J. Wright, Optimization for Machine Learning, Massachusetts Institute of Technology, London, 2012.

G. Szec¨o, Orthogonal polynomials, American mathematical society, colloquium publications, vol. XXIII, 1939.

V. Vapnik, The Nature of Statistical Learning Theory, Springer -Verlag, NewYork, 1995.

L. Wang, Support vector machines: theory and applications, New York, Springer-Verlag, 2005.

N. YE, R. Sun, Y. Liu and L. Cao, Support vector machine with orthogonal Chebyshev kernel, IEEE, 2006.

M. A. Zaky, I. G. Ameen and M. A. Abdelkawy, A new operational matrix based on Jacobi wavelets for a class of variable- order fractional differential equations, Proceedings of the Romanian Academy, series A, Vol. 18, no. 4, pp. 315–322, 2017.

L. Zhang, W. Zhou and L. Jiao, Wavelet support vector machine, IEEE transactions on systems, Man and Cybernetics, Part B, Cybernetics, vol. 34, no. 1, 2004.

F. Zhou, Z. Fang and J. Xu, Constructing support vector machine kernels from orthogonal polynomials for face and speaker verification, Fourth International Conference on Image and Graphics, IEEE, 2007.

Published
2019-12-01
How to Cite
Nadira, A., Abdessamad, A., & Sidi Mohamed, B. (2019). Regularized Jacobi Wavelets Kernel for Support Vector Machines. Statistics, Optimization & Information Computing, 7(4), 669-685. https://doi.org/10.19139/soic-2310-5070-634
Section
Research Articles