Ellipsoidal Subspace Support Vector Data Description
This work addresses one-class classification, a domain-specific problem in outlier detection, with incremental improvements over existing methods.
The paper tackles the problem of one-class classification by proposing a method that transforms data into a low-dimensional subspace optimized for ellipsoidal encapsulation, achieving better results than classic and recent methods in most cases and converging faster than Subspace Support Vector Data Description.
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the data in the subspace; hence, it yields a more generalized solution as compared to Subspace Support Vector Data Description for a hypersphere. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve better results in the majority of cases. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.