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Alternating Direction Multiplier Method for Matrix - Norm Optimization in Multitask Feature Learning Problems

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  • Yaping Hu
  • Liying Liu
  • Yujie Wang

Abstract

The joint feature selection problem can be resolved by solving a matrix - norm minimization problem. For - norm regularization, one of the most fascinating features is that some similar sparsity structures can be employed by multiple predictors. However, the nonsmooth nature of the problem brings great challenges to the problem. In this paper, an alternating direction multiplier method combined with the spectral gradient method is proposed for solving the matrix - norm optimization problem involved with multitask feature learning. Numerical experiments show the effectiveness of the proposed algorithm.

Suggested Citation

  • Yaping Hu & Liying Liu & Yujie Wang, 2020. "Alternating Direction Multiplier Method for Matrix - Norm Optimization in Multitask Feature Learning Problems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-7, August.
    • Handle: RePEc:hin:jnlmpe:4864296
    DOI: 10.1155/2020/4864296
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