mlsanityData to 7 Oct 2026

Subspace Support Vector Data Description

arXiv 1802.03989 ↗First posted 12 Feb 2018cs.CVcited by 31

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Abstract (arXiv)

This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2018.

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Citing papers per year: 2021: 4; 2022: 4; 2023: 8; 2024: 7; 2025: 5; 2026: 3.

31 papers.

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  1. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  2. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  3. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  4. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 85
  5. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  6. 2023Delving into CLIP latent space for Video Anomaly Recognitioncited by 27
  7. 2022Towards Open Set Video Anomaly Detectioncited by 26
  8. 2025VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Treecited by 15
  9. 2021Graph-Embedded Subspace Support Vector Data Descriptioncited by 13
  10. 2023A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detectioncited by 13
  11. 2021AnomalyHop: An SSL-based Image Anomaly Localization Methodcited by 12
  12. 2022Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiographycited by 9
19 more
  1. 2025A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysiscited by 9
  2. 2024Cross-Domain Learning for Video Anomaly Detection with Limited Supervisioncited by 6
  3. 2023Newton Method-based Subspace Support Vector Data Descriptioncited by 5
  4. 2024Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detectioncited by 5
  5. 2023Hyperspectral Image Analysis with Subspace Learning-based One-Class Classificationcited by 4
  6. 2023Credit Card Fraud Detection with Subspace Learning-based One-Class Classificationcited by 3
  7. 2023One-Class Classification for Intrusion Detection on Vehicular Networkscited by 2
  8. 2024Trustworthiness of mathbbX Users: A One-Class Classification Approachcited by 2
  9. 2024Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classificationcited by 2
  10. 2025The Role of Video Generation in Enhancing Data-Limited Action Understandingcited by 2
  11. 2026TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learningcited by 2
  12. 2023Convolutional autoencoder-based multimodal one-class classificationcited by 1
  13. 2024Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Descriptioncited by 1
  14. 2025Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approachcited by 1
  15. 2026CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detectioncited by 1
  16. 2021Generalized One-Class Learning Using Pairs of Complementary Classifierscited by 0
  17. 2024Linear-time One-Class Classification with Repeated Element-wise Foldingcited by 0
  18. 2025To Vaccinate or not to Vaccinate? Analyzing mathbbX Power over the Pandemiccited by 0
  19. 2026Decomposing one-class support vector machine into an ensemble of one-data support vector machinescited by 0