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      ℓp-norm support vector data description

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      Embargo Lift Date: 2024-07-23
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      Author(s)
      Arashloo, Shervin Rahimzadeh
      Date
      2022-07-23
      Source Title
      Pattern Recognition
      Print ISSN
      0031-3203
      Electronic ISSN
      1873-5142
      Publisher
      Elsevier BV
      Volume
      132
      Pages
      108930- 1 - 108930- 11
      Language
      English
      Type
      Article
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      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 the errors/slacks via an ℓ1-norm penalty term. In this study, we generalise this modelling formalism to a general ℓp-norm (p ≥ 1) penalty function on slacks. By virtue of an ℓp-norm function, in the primal space, the proposed approach enables formulating a non-linear cost for slacks. From a dual problem perspective, the proposed method introduces a dual norm into the objective function, thus, proving a controlling mechanism to tune into the intrinsic sparsity/uniformity of the problem for enhanced descriptive capability. A theoretical analysis based on Rademacher complexities characterises the generalisation performance of the proposed approach while the experimental results on several datasets confirm the merits of the proposed method compared to other alternatives.
      Keywords
      One-class classification
      Kernel methods
      Support vector data description
      ℓp -norm penalty
      Permalink
      http://hdl.handle.net/11693/111326
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.patcog.2022.108930
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      • Department of Computer Engineering 1561
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