# Ellipsoidal Subspace Support Vector Data Description

arXiv: https://arxiv.org/abs/2003.09504 · HTML: https://mlsanity.com/paper/2003.09504/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [Graph-Embedded Subspace Support Vector Data Description](https://mlsanity.com/paper/2104.14370/) (2021)
- [Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography](https://mlsanity.com/paper/2204.07253/) (2022)
- [Newton Method-based Subspace Support Vector Data Description](https://mlsanity.com/paper/2309.13960/) (2023)
- [Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification](https://mlsanity.com/paper/2304.09730/) (2023)
- [Improved Active Fire Detection using Operational U-Nets](https://mlsanity.com/paper/2304.09721/) (2023)
- [Credit Card Fraud Detection with Subspace Learning-based One-Class Classification](https://mlsanity.com/paper/2309.14880/) (2023)
- [One-Class Classification for Intrusion Detection on Vehicular Networks](https://mlsanity.com/paper/2309.14134/) (2023)
- [Trustworthiness of mathbbX Users: A One-Class Classification Approach](https://mlsanity.com/paper/2402.02066/) (2024)
- [Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification](https://mlsanity.com/paper/2402.06530/) (2024)
- [Convolutional autoencoder-based multimodal one-class classification](https://mlsanity.com/paper/2309.14090/) (2023)
- [Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach](https://mlsanity.com/paper/2502.15793/) (2025)
- [A Mathematical Optimization Approach to Multisphere Support Vector Data Description](https://mlsanity.com/paper/2507.11106/) (2025)
- [To Vaccinate or not to Vaccinate? Analyzing mathbbX Power over the Pandemic](https://mlsanity.com/paper/2503.02563/) (2025)

## Abstract (arXiv)

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.