Graph-Embedded Subspace Support Vector Data Description

TL;DR

This paper introduces a graph-embedded subspace learning framework for one-class classification, integrating spectral solutions and support vector data description to improve performance over existing methods.

Contribution

It presents a novel graph-embedded subspace learning framework that unifies and extends existing one-class classification techniques with spectral solutions.

Findings

Improved classification performance over baseline methods

Incorporation of spectral solutions enhances model efficiency

Flexible framework allows integration of various optimization goals

Abstract

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 alternatives 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 proposed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification.