Improving the discriminant ability of local margin based learning method by incorporating the global between-class separability criterion

Neurocomputing

Volumn 73, Issue 1-3, 2009, Pages 536-541

  Fang, Bin ^a   Cheng, Miao ^a   Tang, Yuan Yan ^a   He, Guanghui ^a  

^a CHONGQING UNIVERSITY   (China)

Author keywords

Between class separability criterion (BCSC); Feature extraction; Local discriminant analysis; Manifold learning; Marginal Fisher analysis (MFA)

Indexed keywords

BETWEEN-CLASS SEPARABILITY CRITERION (BCSC); CLASS-SEPARABILITY CRITERION; EXPERIMENTAL EVALUATION; FEATURE EXTRACTION AND CLASSIFICATION; FEATURE REPRESENTATION; HIGH DIMENSIONAL DATA; INTRINSIC LOW-DIMENSIONAL MANIFOLDS; LEARNING METHODS; LEARNING PROBLEM; LIMITED DATA; LOCAL DISCRIMINANT ANALYSIS; MACHINE-LEARNING; MANIFOLD LEARNING; MARGINAL FISHER ANALYSIS; MARGINAL FISHER ANALYSIS (MFA); SUPERVISED LEARNING METHODS;

COMPUTER VISION; DISCRIMINANT ANALYSIS; EDUCATION;

FEATURE EXTRACTION;

ANALYTIC METHOD; ARTICLE; CLASSIFICATION; COMPUTER SYSTEM; CONTROLLED STUDY; DATA ANALYSIS; DISCRIMINANT ANALYSIS; EXPERIMENTAL DESIGN; FACIAL EXPRESSION; HUMAN; HUMAN EXPERIMENT; IMAGE ANALYSIS; INTERMETHOD COMPARISON; K NEAREST NEIGHBOR; MACHINE LEARNING; NORMAL HUMAN; PRIORITY JOURNAL; STATISTICAL PARAMETERS;

EID: 70350742991     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.07.016     Document Type: Article

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