  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.
