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<p>In this paper, we consider the numerical solution to the soft margin support vector machine optimization problem. This problem is typically solved using the SMO algorithm, given the high computational complexity of traditional optimization algorithms when dealing with large-scale kernel matrices. In this work, we propose employing an NFFT-accelerated matrix–vector product using an ANOVA decomposition for the feature space. Through this approach, an additive kernel design of trivariate sub-kernels is induced and the total number of used features can be reduced. This is used within an interior point method for the overall optimization problem. As this method requires the solution of a linear system of saddle point form we suggest a preconditioning approach that is based on low-rank approximations of the kernel matrix together with a Krylov subspace solver. We compare the accuracy of the ANOVA-based kernel with the default LIBSVM implementation. We investigate the performance of the different preconditioners as well as the accuracy of the ANOVA kernel on several large-scale data sets.</p>

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A preconditioned interior point method for support vector machines using an ANOVA decomposition and NFFT-based matrix–vector products

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Theresa Wagner,
  • John W. Pearson,
  • Martin Stoll

摘要

In this paper, we consider the numerical solution to the soft margin support vector machine optimization problem. This problem is typically solved using the SMO algorithm, given the high computational complexity of traditional optimization algorithms when dealing with large-scale kernel matrices. In this work, we propose employing an NFFT-accelerated matrix–vector product using an ANOVA decomposition for the feature space. Through this approach, an additive kernel design of trivariate sub-kernels is induced and the total number of used features can be reduced. This is used within an interior point method for the overall optimization problem. As this method requires the solution of a linear system of saddle point form we suggest a preconditioning approach that is based on low-rank approximations of the kernel matrix together with a Krylov subspace solver. We compare the accuracy of the ANOVA-based kernel with the default LIBSVM implementation. We investigate the performance of the different preconditioners as well as the accuracy of the ANOVA kernel on several large-scale data sets.