Efficient semi-supervised feature selection with noise insensitive trace ratio criterion

Neurocomputing

Volumn 105, Issue , 2013, Pages 12-18

  Liu, Yun ^a   Nie, Feiping ^a   Wu, Jigang ^b   Chen, Lihui ^c  

^a University of Texas at Arlington   (United States)

^b TIANJIN POLYTECHNIC UNIVERSITY   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Feature selection; Graph based learning; Re scale preprocessing; Semi supervised; Trace ratio

Indexed keywords

DIMENSIONALITY REDUCTION; FEATURE SELECTION ALGORITHM; GRAPH-BASED LEARNING; HIGH DIMENSIONAL DATA; LABELED AND UNLABELED DATA; RE-SCALE PREPROCESSING; SEMI-SUPERVISED; TRACE RATIO;

ALGORITHMS; FEATURE EXTRACTION;

TRACE ANALYSIS;

ARTICLE; BACK PROPAGATION; CRITERION VARIABLE; DATA ANALYSIS; FEATURE SELECTION; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL COMPUTING; NOISE INSENSITIVE TRACE RATIO CRITERION; PRIORITY JOURNAL; PROBLEM SOLVING; PROCESS OPTIMIZATION;

EID: 84875384168     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.05.031     Document Type: Article

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