DOI码：10.1109/TNNLS.2013.2262180

发表刊物：IEEE Transactions on Neural Networks and Learning Systems

关键字：Homotopy method, piecewise linear solution, regularization path, solution path, support vector machine (SVM).

摘要：This paper proposes a novel ridge-adding-based approach for handling singularities that are frequently encountered in the powerful SVMpath algorithm. Unlike the existing method that performs linear programming as an additional step to track the optimality condition path in a multidimensional feasible space, our new approach provides a simpler and computationally more efficient implementation, which needs no extra time-consuming procedures other than introducing a random ridge term to each data point. Contrary to the existing ridgeadding method, which fails to avoid singularities as the ridge terms tend to zero, our novel approach, for any small random ridge terms, guarantees the existence of the inverse matrix by ensuring that only one index is added into or removed from the active set. The performance of the proposed algorithm, in terms of both computational complexity and the ability of singularity avoidance, is manifested by rigorous mathematical analyses as well as experimental results.

合写作者：徐维超,常春起

第一作者：戴继生

论文类型：期刊论文

卷号：24

期号：11

页面范围：1736-1748

是否译文：否

发表时间：2013-06-19

收录刊物：SCI