$\ell_p$ Slack Norm Support Vector Data Description

• URL: http://arxiv.org/abs/2203.08932v1
• Date: Wed, 16 Mar 2022 20:38:37 GMT
• Title: $\ell_p$ Slack Norm Support Vector Data Description
• Authors: Shervin R. Arashloo
• Abstract summary: We generalise this modelling formalism to a general $ell_p$-norm ($pgeq1$) slack penalty function. By virtue of an $ell_p$ slack norm, the proposed approach enables formulating a non-linear cost function with respect to slacks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The support vector data description (SVDD) approach serves as a de facto standard for one-class classification where the learning task entails inferring the smallest hyper-sphere to enclose target objects while linearly penalising any errors/slacks via an $\ell_1$-norm penalty term. In this study, we generalise this modelling formalism to a general $\ell_p$-norm ($p\geq1$) slack penalty function. By virtue of an $\ell_p$ slack norm, the proposed approach enables formulating a non-linear cost function with respect to slacks. From a dual problem perspective, the proposed method introduces a sparsity-inducing dual norm into the objective function, and thus, possesses a higher capacity to tune into the inherent sparsity of the problem for enhanced descriptive capability. A theoretical analysis based on Rademacher complexities characterises the generalisation performance of the proposed approach in terms of parameter $p$ while the experimental results on several datasets confirm the merits of the proposed method compared to other alternatives.

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