mlsanityData to 7 Oct 2026

Graph-Embedded Subspace Support Vector Data Description

arXiv 2104.14370 ↗First posted 29 Apr 2021cs.LGcited by 13

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose a novel subspace learning framework for one-class classification. The proposed framework presents the problem in the form of graph embedding. It includes the previously proposed subspace one-class techniques as its special cases and provides further insight on what these techniques actually optimize. The framework allows to incorporate other meaningful optimization goals via the graph preserving criterion and reveals a spectral solution and a spectral regression-based solution as alternatives to the previously used gradient-based technique. We combine the subspace learning framework iteratively with Support Vector Data Description applied in the subspace to formulate Graph-Embedded Subspace Support Vector Data Description. We experimentally analyzed the performance of newly proposed different variants. We demonstrate improved performance against the baselines and the recently proposed subspace learning methods for one-class classification.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 7 of its 46 references that are in the map. First posted on arXiv 29 Apr 2021.

Builds on

7 of 46 references.

References · 7

  1. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  2. 2018Subspace Support Vector Data Descriptioncited by 31
  3. 2017Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recoverycited by 25
  4. 2019Multimodal Subspace Support Vector Data Descriptioncited by 15
  5. 2020Ellipsoidal Subspace Support Vector Data Descriptioncited by 13
  6. 2020Boosting rare benthic macroinvertebrates taxa identification with one-class classificationcited by 11
  7. 2021Average Localised Proximity: A new data descriptor with good default one-class classification performancecited by 7

Built on by

Cited by year

Citing papers per year: 2023: 6; 2024: 4; 2025: 3.

13 papers.

Sort:

  1. 2023Newton Method-based Subspace Support Vector Data Descriptioncited by 5
  2. 2023Hyperspectral Image Analysis with Subspace Learning-based One-Class Classificationcited by 4
  3. 2023Improved Active Fire Detection using Operational U-Netscited by 3
  4. 2023Credit Card Fraud Detection with Subspace Learning-based One-Class Classificationcited by 3
  5. 2023One-Class Classification for Intrusion Detection on Vehicular Networkscited by 2
  6. 2024Trustworthiness of mathbbX Users: A One-Class Classification Approachcited by 2
  7. 2024Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classificationcited by 2
  8. 2023Convolutional autoencoder-based multimodal one-class classificationcited by 1
  9. 2024Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Descriptioncited by 1
  10. 2025A Mathematical Optimization Approach to Multisphere Support Vector Data Descriptioncited by 1
  11. 2024Linear-time One-Class Classification with Repeated Element-wise Foldingcited by 0
  12. 2025To Vaccinate or not to Vaccinate? Analyzing mathbbX Power over the Pandemiccited by 0
1 more
  1. 2025Generalized Reference Kernel With Negative Samples For Support Vector One-class Classificationcited by 0