A kernel-based sparsity preserving method for semi-supervised classification

Neurocomputing

Volumn 139, Issue , 2014, Pages 345-356

  Gu, Nannan ^a   Wang, Di ^b   Fan, Mingyu ^b   Meng, Deyu ^c  

^a CAPITAL UNIVERSITY OF ECONOMICS AND BUSINESS   (China)

^b WENZHOU UNIVERSITY   (China)

^c XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Feature extraction; Manifold regularization; Semi supervised classification; Semi supervised learning; Sparse representation

Indexed keywords

COST FUNCTIONS; FEATURE EXTRACTION;

BENCHMARK DATA; DATA CLASSIFICATION; DISCRIMINATIVE ABILITY; EFFECTIVE APPROACHES; MANIFOLD REGULARIZATIONS; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED LEARNING; SPARSE REPRESENTATION;

SUPERVISED LEARNING;

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA ANALYSIS; INTERMETHOD COMPARISON; KERNEL BASED SPARSE REGULARIZATION LEAST SQUARE CLASSIFICATION; KERNEL METHOD; MATHEMATICAL COMPUTING; PRIORITY JOURNAL;

EID: 84900868508     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.02.022     Document Type: Article

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