Robust sparse coding for one-class classification based on correntropy and logarithmic penalty function

作者：

Highlights：

• Robust sparse coding for one-class classification based on correntropy and logarithmic penalty function is proposed.

• The optimization problem of the proposed robust sparse coding is iteratively solved by the half-quadratic optimization technique.

• The generalization performance of robust sparse coding is analyzed from the theoretical analysis.

• The effectiveness of the proposed method is validated on twenty UCI benchmark data sets and one handwritten digit data set.

摘要

•Robust sparse coding for one-class classification based on correntropy and logarithmic penalty function is proposed.•The optimization problem of the proposed robust sparse coding is iteratively solved by the half-quadratic optimization technique.•The generalization performance of robust sparse coding is analyzed from the theoretical analysis.•The effectiveness of the proposed method is validated on twenty UCI benchmark data sets and one handwritten digit data set.

论文关键词：Sparse coding,One-class classification,Logarithmic penalty function,Correntropy,One-class support vector machine

论文评审过程：Received 14 May 2020, Revised 27 August 2020, Accepted 24 September 2020, Available online 28 September 2020, Version of Record 30 September 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107685