Unsupervised feature selection via maximum projection and minimum redundancy

Knowledge-Based Systems

Volumn 75, Issue , 2015, Pages 19-29

  Wang, Shiping ^a,b   Pedrycz, Witold ^b,c   Zhu, Qingxin ^a   Zhu, William ^d  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b UNIVERSITY OF ALBERTA   (Canada)

^c SYSTEMS RESEARCH INSTITUTE   (Poland)

^d MINNAN NORMAL UNIVERSITY   (China)

Author keywords

Feature selection; Kernel method; Machine learning; Matrix factorization; Minimum redundancy; Unsupervised learning

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; DIMENSIONALITY REDUCTION; FACTORIZATION; FEATURE EXTRACTION; ITERATIVE METHODS; LEARNING SYSTEMS; MATRIX ALGEBRA; REDUNDANCY; UNSUPERVISED LEARNING;

FEATURE SELECTION METHODS; GREEDY ALGORITHMS; ITERATIVE UPDATE; KERNEL METHODS; MATRIX FACTORIZATIONS; PROJECTION MATRIX; STATE OF THE ART; UNSUPERVISED FEATURE SELECTION;

CLASSIFICATION (OF INFORMATION);

EID: 84920525835     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.11.008     Document Type: Article

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