An efficient matrix factorization based low-rank representation for subspace clustering

Pattern Recognition

Volumn 46, Issue 1, 2013, Pages 284-292

  Liu, Yuanyuan ^a   Jiao, L C ^a   Shang, Fanhua ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Alternating direction method (ADM); Low rank representation; Matrix tri factorization; Nuclear norm minimization (NNM); Positive semidefinite (PSD)

Indexed keywords

ALTERNATING DIRECTION METHODS; COMPUTATION COSTS; CORRUPTED DATA; LOW RANK; MATRIX FACTORIZATIONS; MINIMIZATION PROBLEMS; NUCLEAR NORM MINIMIZATION (NNM); OPTIMAL SOLUTIONS; POSITIVE SEMIDEFINITE (PSD); REAL WORLD DATA; SMALL SCALE; SPECTRAL CLUSTERING; STATE-OF-THE-ART ALGORITHMS; SUBSPACE CLUSTERING; UNSUPERVISED CLUSTERING; VISION COMMUNITIES;

CLUSTERING ALGORITHMS; COMPUTER VISION; FACTORIZATION; SINGULAR VALUE DECOMPOSITION; VIRTUAL REALITY;

PROBLEM SOLVING;

EID: 84866021257     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.06.011     Document Type: Article

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