Variable selection for model-based high-dimensional clustering and its application to microarray data

Biometrics

Volumn 64, Issue 2, 2008, Pages 440-448

  Wang, Sijian ^a   Zhu, Ji ^b  

^a UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

EM algorithm; High dimension low sample size; Microarray; Model based clustering; Regularization; Variable selection

Indexed keywords

CLUSTERING ALGORITHMS;

EM ALGORITHMS; HIGH-DIMENSION LOW SAMPLE SIZE; HIGH-DIMENSIONAL CLUSTERING; HIGHER DIMENSIONS; L 1 NORM; MODEL-BASED CLUSTERING; MODEL-BASED OPC; REGULARISATION; SAMPLE SIZES; VARIABLES SELECTIONS;

NUMERICAL METHODS;

ARRAY; CLUSTER ANALYSIS; MODELING;

ACUTE GRANULOCYTIC LEUKEMIA; ACUTE LYMPHOBLASTIC LEUKEMIA; ALGORITHM; ARTICLE; BURKITT LYMPHOMA; CHILDHOOD CANCER; CLUSTER ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; EWING SARCOMA; HUMAN; INTERMETHOD COMPARISON; MICROARRAY ANALYSIS; NEUROBLASTOMA; NORMAL DISTRIBUTION; QUANTITATIVE ANALYSIS; RHABDOMYOSARCOMA; SIMULATION; STATISTICAL MODEL; STATISTICAL PARAMETERS;

EID: 43749096785     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2007.00922.x     Document Type: Article

References (27)