Inductive robust principal component analysis

IEEE Transactions on Image Processing

Volumn 21, Issue 8, 2012, Pages 3794-3800

  Bao, Bing Kun ^a,b   Liu, Guangcan ^c   Xu, Changsheng ^a,b   Yan, Shuicheng ^d  

^a INSTITUTE OF AUTOMATION   (China)

^b China Singapore Institute of Digital Media   (Singapore)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^d NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Error correction; robust principal component analysis (PCA); subspace learning

Indexed keywords

COMPUTATIONAL COSTS; CORRUPTED DATA; DATA MATRICES; DATA SETS; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; HUMAN FACES; LEAST SQUARE ERRORS; LOW RANK APPROXIMATIONS; LOW-DIMENSIONAL SUBSPACE; ONLINE COMPUTATIONS; PCA METHOD; POLYNOMIAL-TIME; PROJECTION MATRIX; REGULARIZED MINIMIZATION PROBLEMS; ROBUST PRINCIPAL COMPONENT ANALYSIS; SUBSPACE LEARNING; TRAINING DATA; TRAINING PROCEDURES; VIDEO SURVEILLANCE;

ERROR CORRECTION; MATRIX ALGEBRA; POLYNOMIAL APPROXIMATION; PRINCIPAL COMPONENT ANALYSIS; SECURITY SYSTEMS;

ROBUST CONTROL;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; ARTICLE; METHODOLOGY;

ALGORITHMS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84864138018     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2192742     Document Type: Article

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