Graph-embedded subspace support vector data description

作者：

Highlights：

• A novel subspace learning framework for one-class classification is proposed.

• The framework presents the problem in the form of graph embedding and includes the previously proposed subspace one-class techniques as its special cases.

• The proposed framework reveals spectral and spectral regression-based solutions as alternatives to the previously used gradient-based technique.

• Similarities to the traditional subspace learning techniques and other novel insights revealed by the framework are discussed.

摘要

•A novel subspace learning framework for one-class classification is proposed.•The framework presents the problem in the form of graph embedding and includes the previously proposed subspace one-class techniques as its special cases.•The proposed framework reveals spectral and spectral regression-based solutions as alternatives to the previously used gradient-based technique.•Similarities to the traditional subspace learning techniques and other novel insights revealed by the framework are discussed.

论文关键词：One-Class classification,Support vector data description,Subspace learning,Spectral regression

论文评审过程：Received 4 September 2021, Revised 8 July 2022, Accepted 20 August 2022, Available online 27 August 2022, Version of Record 7 September 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108999