Feature selection and multi-kernel learning for sparse representation on a manifold

Neural Networks

Volumn 51, Issue , 2014, Pages 9-16

  Wang, Jim Jing Yan ^a,b   Bensmail, Halima ^c   Gao, Xin ^a  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c QATAR COMPUTING RESEARCH INSTITUTE   (Qatar)

Author keywords

Data representation; Feature selection; Manifold; Multiple kernel learning; Sparse coding

Indexed keywords

ALGORITHMS; BIOINFORMATICS; FEATURE EXTRACTION; ITERATIVE METHODS; MEDICAL IMAGING;

DATA REPRESENTATIONS; MANIFOLD; MULTI-KERNEL LEARNING; MULTIPLE KERNEL LEARNING; N-LINKED GLYCOSYLATION; NON-LINEAR DISTRIBUTION; SPARSE CODING; SPARSE REPRESENTATION;

GRAPH THEORY;

ARTICLE; BREAST CANCER; CODING; DATA ANALYSIS; GLYCOSYLATION; INFORMATION RETRIEVAL; KERNEL METHOD; LEARNING ALGORITHM; MAMMOGRAPHY; MULTIPLE KERNEL LEARNING; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROCESS OPTIMIZATION; STATISTICAL DISTRIBUTION;

DATA REPRESENTATION; FEATURE SELECTION; MANIFOLD; MULTIPLE KERNEL LEARNING; SPARSE CODING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST; BREAST NEOPLASMS; GLYCOSYLATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LINEAR MODELS; MAMMOGRAPHY; NONLINEAR DYNAMICS; ROC CURVE;

EID: 84890219380     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2013.11.009     Document Type: Article

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