Ellipsoidal Subspace Support Vector Data Description

• URL: http://arxiv.org/abs/2003.09504v1
• Date: Fri, 20 Mar 2020 21:31:03 GMT
• Title: Ellipsoidal Subspace Support Vector Data Description
• Authors: Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
• Abstract summary: We propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. We provide both linear and non-linear formulations for the proposed method. The proposed method is noticed to converge much faster than recently proposed Subspace Support Vector Data Description.
• Score: 98.67884574313292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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