Graph-Embedded Subspace Support Vector Data Description
Fahad Sohrab, Alexandros Iosifidis, Moncef Gabbouj, Jenni Raitoharju

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…
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Taxonomy
TopicsText and Document Classification Technologies
