کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
530051 | 869735 | 2014 | 12 صفحه PDF | دانلود رایگان |
• We solve a primal embedded feature selection problem for nonlinear SVMs.
• Use smooth hinge loss and trust region algorithm for bound constrained minimization.
• Propose alternating optimization approach to break problem into two smaller ones.
• Propose explicit margin maximization to solve feature selection subproblem.
• Show our approach improves state-of-art and other algorithms on various data.
Embedding feature selection in nonlinear support vector machines (SVMs) leads to a challenging non-convex minimization problem, which can be prone to suboptimal solutions. This paper develops an effective algorithm to directly solve the embedded feature selection primal problem. We use a trust-region method, which is better suited for non-convex optimization compared to line-search methods, and guarantees convergence to a minimizer. We devise an alternating optimization approach to tackle the problem efficiently, breaking it down into a convex subproblem, corresponding to standard SVM optimization, and a non-convex subproblem for feature selection. Importantly, we show that a straightforward alternating optimization approach can be susceptible to saddle point solutions. We propose a novel technique, which shares an explicit margin variable to overcome saddle point convergence and improve solution quality. Experiment results show our method outperforms the state-of-the-art embedded SVM feature selection method, as well as other leading filter and wrapper approaches.
Journal: Pattern Recognition - Volume 47, Issue 6, June 2014, Pages 2153–2164