  Sparse Subspace Clustering (SSC) is one of the most popular methods for
clustering data points into their underlying subspaces. However, SSC may suffer
from heavy computational burden. Orthogonal Matching Pursuit applied on SSC
accelerates the computation but the trade-off is the loss of clustering
accuracy. In this paper, we propose a noise-robust algorithm, Restricted
Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering
(RCOMP-SSC), to improve the clustering accuracy and maintain the low
computational time 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-SCC. And the framework is scalable for other data point selection
strategies. Our analysis and experiments on synthetic data and two real-world
databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared
with other clustering methods in terms of accuracy and computational time.
