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Decomposing one-class support vector machine into an ensemble of one-data support vector machines
[Submitted on 14 Jun 2026] · 2026-06-16 · via cs.LG updates on arXiv.org

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Abstract:One-class classification (OCC) is a classification problem in which the training data contains only one class. The one-class support vector machine (OCSVM) is one of the most competitive OCC algorithms. However, OCSVM has scalability issues with large-scale datasets. This paper proposes the acceleration strategy of OCSVM. The idea is to decompose the dataset into samples and train OCSVM models for single data points. Subsequently, ensemble learning is applied to combine all models to compute the OCSVM model for the dataset. In addition, further acceleration is achieved through a data-reduction strategy with an OCSVM model trained on the average of the training samples. The experiment compared the proposal and traditional OCSVM using the Python package. The proposed strategy is faster than traditional OCSVM, while achieving similar classification results. Moreover, the proposed strategy can create one-to-one correspondence between samples and models. Source code is uploaded at this https URL

Submission history

From: Toshitaka Hayashi [view email]
[v1] Sun, 14 Jun 2026 20:05:34 UTC (2,883 KB)

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