Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering
Wenqi Zhu, Shuai Yang, Yuesheng Zhu · IEEE Signal Processing Letters · 2019
Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering a group of data points lying in a union of low-dimensional subspaces. However, SSC may suffer from heavy computational burden. Applying Orthogonal Matching Pursuit (OMP) on SSC may accelerate the computation but the trade-off is the loss of clustering accuracy. In this letter, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and remain computationally efficient by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SSC. The proposed control matrix can be applied to other selection strategies for data point. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) have demonstrated that our algorithm outperforms other methods in clustering accuracy.