$\ell_p$-Norm Multiple Kernel One-Class Fisher Null-Space

• URL: http://arxiv.org/abs/2008.08642v2
• Date: Sat, 25 Sep 2021 14:08:10 GMT
• Title: $\ell_p$-Norm Multiple Kernel One-Class Fisher Null-Space
• Authors: Shervin Rahimzadeh Arashloo
• Abstract summary: The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC) We present a multiple kernel learning algorithm where a general $ell_p$-norm constraint ($pgeq1$) on kernel weights is considered. An extension of the proposed one-class MKL approach is also considered where several related one-class MKL tasks are learned jointly.
• Score: 15.000818334408802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC). For this purpose, based on the Fisher null-space one-class classification principle, we present a multiple kernel learning algorithm where a general $\ell_p$-norm constraint ($p\geq1$) on kernel weights is considered. We cast the proposed one-class MKL task as a min-max saddle point Lagrangian optimisation problem and propose an efficient method to solve it. An extension of the proposed one-class MKL approach is also considered where several related one-class MKL tasks are learned jointly by constraining them to share common kernel weights. An extensive assessment of the proposed method on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.

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