An ADMM Solver for the MKL-$L_{0/1}$-SVM

Yijie Shi, Bin Zhu
3/8/2023
stat.MLcs.LG

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Abstract

We formulate the Multiple Kernel Learning (abbreviated as MKL) problem for the support vector machine with the infamous $(0,1)$-loss function. Some first-order optimality conditions are given and then exploited to develop a fast ADMM solver for the nonconvex and nonsmooth optimization problem. A simple numerical experiment on synthetic planar data shows that our MKL-$L_{0/1}$-SVM framework could be promising.

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Cite this paper

@article{shi2023admm,
  title  = {An ADMM Solver for the MKL-$L_{0/1}$-SVM},
  author = {Yijie Shi and Bin Zhu},
  year   = {2023},
  eprint = {2303.04445},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.04445v2}
}

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