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Orthogonal matching pursuit (OMP) has gained remarkable achievements in the domain of Sparse Subspace Clustering (SSC) for image clustering. However, current methods based on OMP improves the clustering accuracy by adding additional operations, which increase computational complexity. In this paper, a novel SSC algorithm with one-way selective orthogonal matching pursuit (SSC-OWSOMP) is proposed to improve the clustering accuracy without increasing the computational complexity in the SSC-OMP-based methods. In our SSC-OWSOMP, a one-way selective module is designed to avoid mutual selection among data points, which can enrich the information used for clustering without adding additional operations. Experimental results demonstrate that, with the SSC-OWSOMP, not only the clustering accuracy can be improved but also the time complexity be kept, also the SSC-OWSOMP is suitable for the data sets with high sample density.
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