  We describe the Greedy Sparse Subspace Clustering (GSSC) algorithm providing
an efficient method for clustering data belonging to a few low-dimensional
linear or affine subspaces from incomplete corrupted and noisy data. We provide
numerical evidences that, even in the simplest implementation, the greedy
approach increases the subspace clustering capability of the existing
state-of-the art SSC algorithm significantly.
