An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines |
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Authors: | Luca Zanni |
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Institution: | (1) Department of Mathematics, University of Modena and Reggio Emilia, 41100 Modena, Italy |
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Abstract: | In this paper we propose some improvements to a recent decomposition technique for the large quadratic program arising in
training support vector machines. As standard decomposition approaches, the technique we consider is based on the idea to
optimize, at each iteration, a subset of the variables through the solution of a quadratic programming subproblem. The innovative
features of this approach consist in using a very effective gradient projection method for the inner subproblems and a special
rule for selecting the variables to be optimized at each step. These features allow to obtain promising performance by decomposing
the problem into few large subproblems instead of many small subproblems as usually done by other decomposition schemes. We
improve this technique by introducing a new inner solver and a simple strategy for reducing the computational cost of each
iteration. We evaluate the effectiveness of these improvements by solving large-scale benchmark problems and by comparison
with a widely used decomposition package. |
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Keywords: | Support vector machines Quadratic programs Decomposition techniques Gradient projection methods Large-scale problems |
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