Variable Sparsity Kernel Learning

mirrorVSKL Toolbox

IIT-Bombay, IISc, Technion



mirrorVSKL toolbox is a set of matlab scripts which implements the efficient mirror-descent [1,2] based algorithm described in our papers [3,4] for solving various mixed-norm regularization based MKL [5,6,7] formulations --- download link. The experiments outlined in the paper [3] can be repeated using the scripts available here and the preprocessed datasets available at here Please follow the README files for details of usage.



The key features of the algorithm (in contrast with simpleMKL) are:



References:

[1] Aharon Ben-Tal, Tamar Margalit, and Arkadi Nemirovski. The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography. SIAM Journal of Optimization, 12(1):79–108, 2001.

[2] Amir Beck and Marc Teboulle. Mirror descent and nonlinear projected subgradient methods for convex optimization. Operations Research Letters, 31:167–175, 2003.

[3] J. Aflalo, A. Ben-Tal, C. Bhattacharyya, J. Saketha Nath and S. Raman. Variable Sparsity Kernel Learning --- Algorithms and Applications. Submitted to JMLR, 2009. link

[4] J. Saketha Nath, G. Dinesh, S. Raman, C. Bhattacharyya, A. Ben-Tal and K. R. Ramakrishnan. On the Algorithmics and Applications of a mixed-norm regularization based Kernel Learning Formulation. Advances in NIPS, 2009. link

[5] G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El. Ghaoui, and M.I. Jordan. Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research, 5:27–72, 2004.

[6] A. Rakotomamonjy, F. Bach, S. Canu, and Y Grandvalet. SimpleMKL. Journal of Machine Learning Research, 9:2491–2521, 2008.

[7] M. Szafranski, Y. Grandvalet, and A. Rakotomamonjy. Composite Kernel Learning. In Proceedings of the ICML, 2008.



For any comments/bugs please email ramans [AT] csa.iisc.ernet.in.