  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.

Machine Learning;Artificial Intelligence;Machine Learning