Please use this identifier to cite or link to this item: https://doi.org/10.1162/NECO_a_00241
Title: Nondegenerate piecewise linear systems: A finite Newton algorithm and applications in machine learning
Authors: Yuan, X.-T.
Yan, S. 
Issue Date: 2012
Source: Yuan, X.-T.,Yan, S. (2012). Nondegenerate piecewise linear systems: A finite Newton algorithm and applications in machine learning. Neural Computation 24 (4) : 1047-1084. ScholarBank@NUS Repository. https://doi.org/10.1162/NECO_a_00241
Abstract: arise, for example, from the numerical solution of linear complementarity problem, which is useful to model several learning and optimization problems. In this letter, we propose an effective damped Newton method, PLS-DN, to find the exact (up to machine precision) solution of nondegenerate PLSs. PLS-DN exhibits provable semiiterative property, that is, the algorithm converges globally to the exact solution in a finite number of iterations. The rate of convergence is shown to be at least linear before termination. We emphasize the applications of our method in modeling, from a novel perspective of PLSs, some statistical learning problems such as box-constrained least squares, elitist Lasso (Kowalski & Torreesani, 2008), and support vector machines (Cortes & Vapnik, 1995). Numerical results on synthetic and benchmark data sets are presented to demonstrate the effectiveness and efficiency of PLS-DN on these problems. c 2011 Massachusetts Institute of Technology.
Source Title: Neural Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/68001
ISSN: 08997667
DOI: 10.1162/NECO_a_00241
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