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Greedy algorithm for subspace clustering from corrupted and incomplete data

Alexander Petukhov, Inna Kozlov
2015 2015 International Conference on Sampling Theory and Applications (SampTA)  

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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
more » ... cessor 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.
doi:10.1109/sampta.2015.7148933 fatcat:5yiwgv47vfcu5eo5fgnefyr45u
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Alexander Petukhov, Inna Kozlov. "Greedy algorithm for subspace clustering from corrupted and incomplete data." 2015 International Conference on Sampling Theory and Applications (SampTA) (2015)