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On the SVMpath initialization
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DOI：10.1016/j.sigpro.2011.11.020.png)
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Abstract
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This paper presents a novel algorithm for the initial configuration to the model selection problem in the two-class support vector machine (SVM) classification when fitting the entire path of SVM solutions for every value of the regularization parameter. Instead of using quadratic programming for initialization in the conventional two-class SVM regularization path fitting methods, we propose a piecewise linear method which reduces the computational cost significantly. Furthermore, an efficient treatment is provided to deal with the singular case where the data set contains linearly dependent points, duplicate points or nearly duplicate points. The performance of the proposed algorithm in terms of computational complexity and the ability to handle singular cases are backed by strict mathematical analysis and proof, and verified by the experimental results. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Solution path
Regularization path
Piecewise linear solution
Support vector machine (SVM)
Homotopy method
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