Subspace Support Vector Data Description
This addresses one-class classification problems, which are incremental improvements over existing methods.
The paper tackles one-class classification by proposing Subspace Support Vector Data Description, which maps data to an optimized subspace and finds a hypersphere for the target class, resulting in better performance on 14 datasets compared to baselines and recent methods.
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.