Greedy algorithm for subspace clustering from corrupted and incomplete data

We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm providing an efficient method for clustering data belonging to a few low-dimensional linear or affine subspaces. FGSSC is a modification of the SSC algorithm. The main difference of our algorithm from predecessors is its ability to work with noisy data having a high rate of erasures (missed entries at the known locations) and errors (corrupted entries at unknown locations). The algorithm has significant advantage over predecessor on synthetic models as well as for the Extended Yale B dataset of facial images. In particular, the face recognition misclassification rate turned out to be 6–20 times lower than for the SSC algorithm.