Robust semi-supervised nonnegative matrix factorization for image clustering

作者：

Highlights：

• A novel correntropy based semi-supervised NMF method is proposed for image clustering.

• The proposed method is analysed in terms of convergence, robustness, and computational complexity.

• The relationships between the proposed method and several typical NMF based methods are discussed.

• Experimental results show the effectiveness and robustness of the proposed method in image clustering tasks.

摘要

•A novel correntropy based semi-supervised NMF method is proposed for image clustering.•The proposed method is analysed in terms of convergence, robustness, and computational complexity.•The relationships between the proposed method and several typical NMF based methods are discussed.•Experimental results show the effectiveness and robustness of the proposed method in image clustering tasks.

论文关键词：Nonnegative matrix factorization,Supervised information,Correntropy,Outliers,Image clustering

论文评审过程：Received 30 December 2019, Revised 18 August 2020, Accepted 24 September 2020, Available online 25 September 2020, Version of Record 19 October 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107683