American Institute of Mathematical Sciences

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January  2016, 12(1): 285-301. doi: 10.3934/jimo.2016.12.285

Subgradient-based neural network for nonconvex optimization problems in support vector machines with indefinite kernels

Fengqiu Liu 1, and  Xiaoping Xue 2,

1. 

Department of Mathematics, Harbin University of Science and Technology, Harbin, 150080, China
2. 

Department of Mathematics, Harbin Institute of Technology, Harbin 150001

Received  June 2012 Revised  January 2015 Published  April 2015

Support vector machines (SVMs) with positive semidefinite kernels yield convex quadratic programming problems. SVMs with indefinite kernels yield nonconvex quadratic programming problems. Most optimization methods for SVMs rely on the convexity of objective functions and are not efficient for solving such nonconvex problems. In this paper, we propose a subgradient-based neural network (SGNN) for the problems cast by SVMs with indefinite kernels. It is shown that the state of the proposed neural network has finite length, and as a consequence it converges toward a singleton. The coincidence between the solution and the slow solution of SGNN is also proved starting from the initial value of SGNN. Moreover, we employ the Łojasiewicz inequality to exploit the convergence rate of trajectory of SGNN. The obtained results show that each trajectory is either exponentially convergent, or convergent in finite time, toward a singleton belonging to the set of constrained critical points through a quantitative evaluation of the Łojasiewicz exponent at the equilibrium points. This method is easy to implement without adding any new parameters. Three benchmark data sets from the University of California, Irvine machine learning repository are used in the numerical tests. Experimental results show the efficiency of the proposed neural network.
Keywords: support vector machine., indefinite kernels, neural networks, Sub-gradient, Łojasiewicz inequality.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Fengqiu Liu, Xiaoping Xue. Subgradient-based neural network for nonconvex optimization problems in support vector machines with indefinite kernels. Journal of Industrial & Management Optimization, 2016, 12 (1) : 285-301. doi: 10.3934/jimo.2016.12.285
References:
[1]

J. P. Aubin and A. Cellina, Differential Inclusions,, Springer-Verlag, (1984).  doi: 10.1007/978-3-642-69512-4.  Google Scholar

[2]

J. P. Aubin and H. Frankowska, Set-Valued Analysis,, MA: Birkauser, (1990).   Google Scholar

[3]

E. K. P. Chong, S. Hui and S. H. Zak, An analysis of a class of neural networks for solving linear programming problems,, Automatic Control, 44 (1999), 1995.  doi: 10.1109/9.802909.  Google Scholar

[4]

J. Chen and J. Ye, Training SVM with indefinite kernels,, In Proceedings of the 25th International Conference on Machine Learning, (2008), 136.  doi: 10.1145/1390156.1390174.  Google Scholar

[5]

F. H. Clarke, Optimization and Non-smooth Analysis,, Wiley, (1969).   Google Scholar

[6]

L. Fengqiu and X. Xiaoping, Design of natural classification kernels using prior knowledge,, Fuzzy Systems, 20 (2012), 135.   Google Scholar

[7]

A. F. Filippove, Differential Equations with Discontinuous Right-Hand Side, Mathematics and Its Applications (Soviet Series),, Boston, (1988).   Google Scholar

[8]

M. Forti, P. Nistri and M. Quincampoix, Convergence of neural networks for programming problems via a nonsmooth Łojasiewicz inequality,, Neural Networks, 17 (2006), 1471.   Google Scholar

[9]

M. Forti and A. Tesi, Absolute stability of analytic neural networks: An approach based on finite trajectory length,, Circuits System I, 51 (2004), 2460.  doi: 10.1109/TCSI.2004.838143.  Google Scholar

[10]

B. Haasdonk, Feature space interpretation of svms with indefinite kernels,, Pattern Analysis and Machine Intelligence, 27 (2005), 482.  doi: 10.1109/TPAMI.2005.78.  Google Scholar

[11]

M. Hein and O. Bousquet, Hilbertian metrics and positive definite kernels on probability measures,, In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, (2005), 136.   Google Scholar

[12]

J. J. Hopfield and D. W. Tank, "Neural" computation of decisions in optimization problems,, Biological cybernetics, 52 (1985), 141.   Google Scholar

[13]

R. Luss and A. d'Aspremon, Support vector machine classification with indefinite kernels,, Mathematical Programming Computation, 1 (2009), 97.  doi: 10.1007/s12532-009-0005-5.  Google Scholar

[14]

I. Mierswa, Making Indefinite Kernel Learning Practical,, Technical report, (2006).   Google Scholar

[15]

J. C. Platt, Fast training of support vector machines using sequential minimal optimization,, In Advances in Kernel Methods, (1999), 185.   Google Scholar

[16]

T. Rockafellar and R. Wets, Variational Analysis,, Springer-Verlag, (1998).  doi: 10.1007/978-3-642-02431-3.  Google Scholar

[17]

S. Shalev-Shwartz, Y. Singer, N. Srebro and A. Cotter, Pegasos: Primal estimated sub-gradient solver for SVM,, Math. Program., 127 (2011), 3.  doi: 10.1007/s10107-010-0420-4.  Google Scholar

[18]

H. Saigo, J. P. Vert, N. Ueda and T. Akutsu, Protein homology detection using string alignment kernels,, Bioinformatics, 20 (2004), 1682.  doi: 10.1093/bioinformatics/bth141.  Google Scholar

[19]

B. Schölkopf and A. J. Smola, Learning with Kernels,, MIT, (2002).   Google Scholar

[20]

B. Schölkopf, P. Simard, A. J. Smola and V. N. Vapnik, Prior Knowledge in Support Vector Kernels,, MIT, (1998).   Google Scholar

[21]

V. N. Vapnik, Statistical Learning Theory,, Wiley, (1998).   Google Scholar

[22]

B. Wei and X. Xiaoping, Subgradient-based neural networks for nonsmooth nonconvex optimization problems,, Circuits and Systems I: Regular Papers, 55 (2008), 2378.  doi: 10.1109/TCSI.2008.920131.  Google Scholar

[23]

Y. Xia and J. Wang, A one-layer recurrent neural network for support vector machine learning,, Systems, 34 (2004), 1621.  doi: 10.1109/TSMCB.2003.822955.  Google Scholar

[24]

S. Ziye and J. Qingwei, Second order optimality conditions and reformulations for nonconvex quadratically constrained quadratic programming problems,, Journal of Industrial and Management Optimization, 10 (2014), 871.  doi: 10.3934/jimo.2014.10.871.  Google Scholar

show all references

References:
[1]

J. P. Aubin and A. Cellina, Differential Inclusions,, Springer-Verlag, (1984).  doi: 10.1007/978-3-642-69512-4.  Google Scholar

[2]

J. P. Aubin and H. Frankowska, Set-Valued Analysis,, MA: Birkauser, (1990).   Google Scholar

[3]

E. K. P. Chong, S. Hui and S. H. Zak, An analysis of a class of neural networks for solving linear programming problems,, Automatic Control, 44 (1999), 1995.  doi: 10.1109/9.802909.  Google Scholar

[4]

J. Chen and J. Ye, Training SVM with indefinite kernels,, In Proceedings of the 25th International Conference on Machine Learning, (2008), 136.  doi: 10.1145/1390156.1390174.  Google Scholar

[5]

F. H. Clarke, Optimization and Non-smooth Analysis,, Wiley, (1969).   Google Scholar

[6]

L. Fengqiu and X. Xiaoping, Design of natural classification kernels using prior knowledge,, Fuzzy Systems, 20 (2012), 135.   Google Scholar

[7]

A. F. Filippove, Differential Equations with Discontinuous Right-Hand Side, Mathematics and Its Applications (Soviet Series),, Boston, (1988).   Google Scholar

[8]

M. Forti, P. Nistri and M. Quincampoix, Convergence of neural networks for programming problems via a nonsmooth Łojasiewicz inequality,, Neural Networks, 17 (2006), 1471.   Google Scholar

[9]

M. Forti and A. Tesi, Absolute stability of analytic neural networks: An approach based on finite trajectory length,, Circuits System I, 51 (2004), 2460.  doi: 10.1109/TCSI.2004.838143.  Google Scholar

[10]

B. Haasdonk, Feature space interpretation of svms with indefinite kernels,, Pattern Analysis and Machine Intelligence, 27 (2005), 482.  doi: 10.1109/TPAMI.2005.78.  Google Scholar

[11]

M. Hein and O. Bousquet, Hilbertian metrics and positive definite kernels on probability measures,, In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, (2005), 136.   Google Scholar

[12]

J. J. Hopfield and D. W. Tank, "Neural" computation of decisions in optimization problems,, Biological cybernetics, 52 (1985), 141.   Google Scholar

[13]

R. Luss and A. d'Aspremon, Support vector machine classification with indefinite kernels,, Mathematical Programming Computation, 1 (2009), 97.  doi: 10.1007/s12532-009-0005-5.  Google Scholar

[14]

I. Mierswa, Making Indefinite Kernel Learning Practical,, Technical report, (2006).   Google Scholar

[15]

J. C. Platt, Fast training of support vector machines using sequential minimal optimization,, In Advances in Kernel Methods, (1999), 185.   Google Scholar

[16]

T. Rockafellar and R. Wets, Variational Analysis,, Springer-Verlag, (1998).  doi: 10.1007/978-3-642-02431-3.  Google Scholar

[17]

S. Shalev-Shwartz, Y. Singer, N. Srebro and A. Cotter, Pegasos: Primal estimated sub-gradient solver for SVM,, Math. Program., 127 (2011), 3.  doi: 10.1007/s10107-010-0420-4.  Google Scholar

[18]

H. Saigo, J. P. Vert, N. Ueda and T. Akutsu, Protein homology detection using string alignment kernels,, Bioinformatics, 20 (2004), 1682.  doi: 10.1093/bioinformatics/bth141.  Google Scholar

[19]

B. Schölkopf and A. J. Smola, Learning with Kernels,, MIT, (2002).   Google Scholar

[20]

B. Schölkopf, P. Simard, A. J. Smola and V. N. Vapnik, Prior Knowledge in Support Vector Kernels,, MIT, (1998).   Google Scholar

[21]

V. N. Vapnik, Statistical Learning Theory,, Wiley, (1998).   Google Scholar

[22]

B. Wei and X. Xiaoping, Subgradient-based neural networks for nonsmooth nonconvex optimization problems,, Circuits and Systems I: Regular Papers, 55 (2008), 2378.  doi: 10.1109/TCSI.2008.920131.  Google Scholar

[23]

Y. Xia and J. Wang, A one-layer recurrent neural network for support vector machine learning,, Systems, 34 (2004), 1621.  doi: 10.1109/TSMCB.2003.822955.  Google Scholar

[24]

S. Ziye and J. Qingwei, Second order optimality conditions and reformulations for nonconvex quadratically constrained quadratic programming problems,, Journal of Industrial and Management Optimization, 10 (2014), 871.  doi: 10.3934/jimo.2014.10.871.  Google Scholar

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