Graph self-representation method for unsupervised feature selection

Neurocomputing

Volumn 220, Issue , 2017, Pages 130-137

  Hu, Rongyao ^a   Zhu, Xiaofeng ^a   Cheng, Debo ^a   He, Wei ^a   Yan, Yan ^b   Song, Jingkuan ^b   Zhang, Shichao ^a  

^a GUANGXI NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF TRENTO   (Italy)

Author keywords

Alternating direction method multipliers; Dimensionality reduction; Locality preserving projection; Self representation

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; COMPUTER VISION; LEARNING SYSTEMS;

1-NORM REGULARIZATIONS; ALTERNATING DIRECTION METHODS; DIMENSIONALITY REDUCTION; FEATURE SELECTION METHODS; HIGH DIMENSIONAL DATA; LOCALITY PRESERVING PROJECTIONS; SELF-REPRESENTATION; UNSUPERVISED FEATURE SELECTION;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; CORRELATION COEFFICIENT; DATA MINING; GRAPH SELF REPRESENTATION; HUMAN; KERNEL METHOD; LEARNING; MULTIFACTOR DIMENSIONALITY REDUCTION; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SELF CONCEPT; SUPPORT VECTOR MACHINE; UNSUPERVISED FEATURE SELECTION; UNSUPERVISED MACHINE LEARNING;

EID: 84979702281     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2016.05.081     Document Type: Article

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