  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
spectral and spectral regression-based solutions 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.
