Low rank representation with adaptive distance penalty for semi-supervised subspace classification

作者：

Highlights：

• A novel LRR with adaptive distance penalty method is proposed for SSL.

• LRRADP can better capture both the global structure and local affinity of the data.

• The projected based LRRADP(LRRADP2) shows impressive robustness.

• Extensive experiments demonstrate the effectiveness of the proposed method.

摘要

•A novel LRR with adaptive distance penalty method is proposed for SSL.•LRRADP can better capture both the global structure and local affinity of the data.•The projected based LRRADP(LRRADP2) shows impressive robustness.•Extensive experiments demonstrate the effectiveness of the proposed method.

论文关键词：Low rank representation,Adaptive distance penalty,Similarity graph construction,Semi-supervised classification,Projection of LRRADP

论文评审过程：Received 13 January 2016, Revised 3 November 2016, Accepted 11 February 2017, Available online 13 February 2017, Version of Record 21 February 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.017