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.Comment: 6 pages, submitted/accepted, ICPR 201