Subspace Support Vector Data Description

TL;DR

This paper introduces a new one-class classification method called Subspace Support Vector Data Description, which maps data into an optimized subspace to better enclose target classes with a hypersphere, improving performance.

Contribution

It presents a novel subspace mapping approach for one-class classification that iteratively optimizes data representation and description, including linear and non-linear variants.

Findings

Outperforms baseline methods on 14 datasets

Provides a compact low-dimensional class representation

Offers both linear and non-linear mapping options

Abstract

This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.