An effective framework for supervised dimension reduction

Neurocomputing

Volumn 139, Issue , 2014, Pages 397-407

  Than, Khoat ^a   Ho, Tu Bao ^b,d   Nguyen, Duy Khuong ^b,c  

^a HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Viet Nam)

^b JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^c VIETNAM NATIONAL UNIVERSITY   (Viet Nam)

^d VIETNAM NATIONAL UNIVERSITY   (Viet Nam)

Author keywords

Local structure; Manifold learning; Scalability; Supervised dimension reduction; Topic models

Indexed keywords

ENCODING (SYMBOLS);

LOCAL STRUCTURE; LOW-DIMENSIONAL SPACES; MANIFOLD LEARNING; NONLINEAR MANIFOLDS; STATE-OF-THE-ART METHODS; SUPERVISED DIMENSION REDUCTIONS; TOPIC MODEL; UNSUPERVISED METHOD;

SCALABILITY;

ALGORITHM; ARTICLE; INFORMATION; MACHINE LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; STATISTICAL MODEL; SUPERVISED DIMENSION REDUCTION;

EID: 84900990295     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.02.017     Document Type: Article

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