Sparse representation for tumor classification based on feature extraction using latent low-rank representation

BioMed Research International

Volumn 2014, Issue , 2014, Pages

  Gan, Bin ^a   Zheng, Chun Hou ^a,b   Zhang, Jun ^b   Wang, Hong Qiang ^c  

^a QUFU NORMAL UNIVERSITY   (China)

^b ANHUI UNIVERSITY   (China)

^c INSTITUTE OF INTELLIGENT MACHINES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLINICAL EFFECTIVENESS; COMPARATIVE STUDY; DATA EXTRACTION; GENE EXPRESSION; HUMAN; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR; MACHINE LEARNING; NOISE REDUCTION; SPARSE REPRESENTATION CLASSIFICATION; SUPPORT VECTOR MACHINE; TUMOR CLASSIFICATION; TUMOR MODEL; ALGORITHM; CLASSIFICATION; DATA BASE; GENETICS; NEOPLASM; TUMOR GENE;

DNA;

ALGORITHMS; DATABASES AS TOPIC; DNA, NEOPLASM; GENES, NEOPLASM; HUMANS; NEOPLASMS;

EID: 84896109587     PISSN: 23146133     EISSN: 23146141     Source Type: Journal    
DOI: 10.1155/2014/420856     Document Type: Article

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