Theoretical Analysis of Sparse Subspace Clustering with Missing Entries

ICML 2018 Manolis C. TsakirisRene Vidal

Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning method for clustering data lying close to an unknown union of low-dimensional linear subspaces; a problem with numerous applications in pattern recognition and computer vision. Even though the behavior of SSC for complete data is by now well-understood, little is known about its theoretical properties when applied to data with missing entries... (read more)

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