Graph-emb e dde d subspace support vector data description

In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-class techniques as its special cases and provides further insight on what these techniques actually optimize. The framework allows to incorporate other meaningful optimization goals via the graph preserving criterion and reveals a spectral solution and a spectral regression-based solution as alterna-tives to the previously used gradient-based technique. We combine the subspace learning framework iteratively with Support Vector Data Description applied in the subspace to formulate Graph-Embedded Subspace Support Vector Data Description. We experimentally analyzed the performance of newly pro-posed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

In this paper, a novel subspace learning framework for one-class classification is proposed, which presents the problem in the form of graph embedding. The framework includes the previously proposed subspace one-class techniques as special cases and provides further insight on optimization goals. It allows for the incorporation of other meaningful optimization goals and offers alternative solutions to the previously used gradient-based technique. Experimental results demonstrate improved performance compared to baselines and recently proposed methods.