Structured sparse support vector machine with ordered features.

ABSTRACT: In the application of high-dimensional data classification, several attempts have been made to achieve variable selection by replacing the ℓ2 -penalty with other penalties for the support vector machine (SVM). However, these high-dimensional SVM methods usually do not take into account the special structure among covariates (features). In this article, we consider a classification problem, where the covariates are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. We propose a structured sparse SVM to tackle this type of problems, which combines the non-convex penalty and cubic spline estimation procedure (i.e. penalizing second-order derivatives of the coefficients) to the SVM. From a theoretical point of view, the propo

SUBMITTER: Fang K 

PROVIDER: S-EPMC9041777 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature