Similarity preserving low-rank representation for enhanced data representation and effective subspace learning

Neural Networks

Volumn 53, Issue , 2014, Pages 81-94

  Zhang, Zhao ^a,b   Yan, Shuicheng ^b   Zhao, Mingbo ^c  

^a SOOCHOW UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Enhanced representation; Feature learning; Laplacian regularization; Low rank representation; Similarity preservation; Subspace recovery

Indexed keywords

ENHANCED REPRESENTATION; FEATURE LEARNING; LAPLACIAN REGULARIZATIONS; LOW-RANK REPRESENTATIONS; SUBSPACE RECOVERIES;

FEATURE EXTRACTION;

LAPLACE TRANSFORMS;

ARTICLE; BRAIN FUNCTION; CLINICAL EFFECTIVENESS; CONCEPTUAL FRAMEWORK; DATA ANALYSIS; FACIAL EXPRESSION; FUNCTIONAL ASSESSMENT; HUMAN; IMAGE ANALYSIS; INFORMATION PROCESSING; LEARNING; LOW RANK REPRESENTATION; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PERCEPTIVE DISCRIMINATION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS MODEL; PROCESS OPTIMIZATION; QUALITATIVE ANALYSIS; VISUAL MEMORY; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; PROCEDURES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; PATTERN RECOGNITION, AUTOMATED;

EID: 84896701451     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2014.01.001     Document Type: Article

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