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Subspace clustering methods have been used for unsupervised learning and Sparse Subspace Clustering (SSC) is one of the most popular methods. Since ℓ1 optimization in SSC requires complex calculation, Orthogonal Matching Pursuit (OMP) is adopted in OMP-SSC to reduce calculation time, but its performance is unsatisfactory. In this paper, a new algorithm, Orthogonal Matching Pursuit with Adaptive Restriction for Sparse Subspace Clustering (OMPAR-SSC) is proposed, in which two adaptive restrictions varying with the strength or density of connections are developed. Our algorithm can improve the connectivity of the affinity graph and enhance the segmentation effect. Experiments on both synthetic data and real-world data also demonstrate that OMPAR-SSC outperforms other subspace clustering algorithms in terms of accuracy and achieves a good trade-off between efficiency and effectiveness.
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