Structured general and specific multi-view subspace clustering

作者：

Highlights：

• We propose a structured general and specific multi-view subspace clustering method for image clustering.

• The structural general representation matrix keeps the similarity relationship of data and the specific representation matrices exploit the diversity between different matrices.

• We present an effective optimization algorithm to solve the proposed objective function.

• Compared with most state-of-the-arts, experimental results demonstrate that our proposed methods obtain superior performances on four benchmark datasets.

摘要

•We propose a structured general and specific multi-view subspace clustering method for image clustering.•The structural general representation matrix keeps the similarity relationship of data and the specific representation matrices exploit the diversity between different matrices.•We present an effective optimization algorithm to solve the proposed objective function.•Compared with most state-of-the-arts, experimental results demonstrate that our proposed methods obtain superior performances on four benchmark datasets.

论文关键词：Subspace clustering,Multi-view learning,Structure consistence,Diversity

论文评审过程：Received 17 July 2018, Revised 22 April 2019, Accepted 1 May 2019, Available online 4 May 2019, Version of Record 9 May 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.05.005