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MKL-L0/1-SVM

arXiv 2308.12016 ↗First posted 23 Aug 2023stat.ML cs.LGcited by 0

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Abstract (arXiv)

This paper presents a Multiple Kernel Learning (abbreviated as MKL) framework for the Support Vector Machine (SVM) with the (0, 1) loss function. Some KKT-like first-order optimality conditions are provided and then exploited to develop a fast ADMM algorithm to solve the nonsmooth nonconvex optimization problem. Numerical experiments on real data sets show that the performance of our MKL-L0/1-SVM is comparable with the one of the leading approaches called SimpleMKL developed by Rakotomamonjy, Bach, Canu, and Grandvalet [Journal of Machine Learning Research, vol. 9, pp. 2491-2521, 2008].

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 23 Aug 2023.

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8 of 22 references.

References · 8

  1. 2015Global Convergence of ADMM in Nonconvex Nonsmooth Optimizationcited by 98
  2. 2013Projection onto the probability simplex: An efficient algorithm with a simple proof, and an applicationcited by 65
  3. 2014Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problemscited by 64
  4. 2014Global convergence of splitting methods for nonconvex composite optimizationcited by 19
  5. 2018The proximal alternating direction method of multipliers in the nonconvex setting: convergence analysis and ratescited by 13
  6. 2019Global and Quadratic Convergence of Newton Hard-Thresholding Pursuitcited by 9
  7. 2021Quadratic Convergence of Smoothing Newton's Method for 0/1 Loss Optimizationcited by 3
  8. 2023An ADMM Solver for the MKL-L0/1-SVMcited by 1

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