Ellipsoidal data description

Wang, Kunzhe; Xiao, Hanguang

doi:10.1016/j.neucom.2017.01.070

Cited by 7 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An ellipsoid, unlike a hypersphere, takes into account the difference in variance for each dimension as well as covariance between them. A hypersphere, characterized only by a radius and a center will result in superfluous regions which do not contain any target objects in the input space [28]. Ellipsoids with a minimum volume containing the target data have applications spanning over many different fields.…”

Section: Introductionmentioning

confidence: 99%

Ellipsoidal Subspace Support Vector Data Description

et al. 2020

View full text Add to dashboard Cite

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 the data description in the subspace by hyperspherical encapsulation of target class data. 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 competing results and show clear improvement compared to the other support vector based methods. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.

show abstract

Section: Introductionmentioning

confidence: 99%

Ellipsoidal Subspace Support Vector Data Description

et al. 2020

View full text Add to dashboard Cite

show abstract

“…An ellipsoid, unlike a hypersphere, takes into account the difference in variance for each dimension as well as covariance between them. A hypersphere, characterized only by a radius and a center will result in superfluous regions which do not contain any target objects in the input space [27]. Ellipsoids with a minimum volume containing the target data have applications spanning over many different fields.…”

Section: Introductionmentioning

confidence: 99%

Ellipsoidal Subspace Support Vector Data Description

Sohrab,

Raitoharju,

Iosifidis

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract