Subspace segmentation via self-regularized latent K-means

作者：

Highlights：

• A self-regularized latent K-means method was proposed for subspace segmentation.

• The relationships between FRR-related algorithms SRLKM were built.

• An optimization algorithm was proposed to solve SRLKM problem.

摘要

•A self-regularized latent K-means method was proposed for subspace segmentation.•The relationships between FRR-related algorithms SRLKM were built.•An optimization algorithm was proposed to solve SRLKM problem.

论文关键词：Subspace segmentation,Low-rank representation,Matrix factorization,K-means

论文评审过程：Received 22 December 2018, Revised 31 May 2019, Accepted 22 June 2019, Available online 23 June 2019, Version of Record 29 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.047