Multi-view low-rank sparse subspace clustering
作者：
Highlights：
• A multi-view low-rank plus sparse subspace clustering algorithm is proposed.
• Agreements are enforced between representations of the pairs of views or towards a common centroid.
• Constrained convex optimization problem is for each view solved using alternating direction method of multipliers.
• By solving related problem in reproducing kernel Hilbert space, kernel extension of the algorithm is derived.
• Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art multi-view subspace clustering algorithms.
摘要
•A multi-view low-rank plus sparse subspace clustering algorithm is proposed.•Agreements are enforced between representations of the pairs of views or towards a common centroid.•Constrained convex optimization problem is for each view solved using alternating direction method of multipliers.•By solving related problem in reproducing kernel Hilbert space, kernel extension of the algorithm is derived.•Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art multi-view subspace clustering algorithms.
论文关键词：Subspace clustering,Multi-view data,Low-rank,Sparsity,Alternating direction method of multipliers,Reproducing kernel Hilbert space
论文评审过程：Received 13 March 2017, Revised 10 July 2017, Accepted 23 August 2017, Available online 24 August 2017, Version of Record 18 September 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.08.024
