Laplacian Regularized Low-Rank Representation and Its Applications

Abstract

Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. For a given set of observed data corrupted with sparse errors, LRR aims at learning a lowest-rank representation of all data jointly. LRR has broad applications in pattern recognition… (More)
DOI: 10.1109/TPAMI.2015.2462360

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