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State-of-the-art algorithms for sparse subspace clustering perform spectralclustering on a similarity matrix typically obtained by representing each datapoint as a sparse combination of other points using either basis pursuit (BP)or orthogonal matching pursuit (OMP). BP-based methods are often prohibitive inpractice while the performance of OMP-based schemes are unsatisfactory,especially in settings where data points are highly similar. In this paper, wepropose a novel algorithm that exploits an accelerated variant of orthogonalleast-squares to efficiently find the underlying subspaces. We show that undercertain conditions the proposed algorithm returns a subspace-preservingsolution. Simulation results illustrate that the proposed method comparesfavorably with BP-based method in terms of running time while beingsignificantly more accurate than OMP-based schemes.

Accelerated Sparse Subspace Clustering