A simple model-based approach to variable selection in classification and clustering

Clustering and classification of replicated data is often performed using classical techniques that inappropriately treat the data as unreplicated, or by complex modern ones that are computationally demanding. In this paper, we introduce a simple approach based on a spike-and-slab mixture model that is fast, automatic, allows classification, clustering and variable selection in a single framework, and can handle replicated or unreplicated data. Simulation shows that our approach compares well with other recently proposed methods. The ideas are illustrated by application to microarray and metabolomic data. The Canadian Journal of Statistics 43: 157-175; 2015 (c) 2015 Statistical Society of Canada


Published in:
Canadian Journal Of Statistics-Revue Canadienne De Statistique, 43, 2, 157-175
Year:
2015
Publisher:
Hoboken, Wiley-Blackwell
ISSN:
0319-5724
Keywords:
Laboratories:




 Record created 2015-09-28, last modified 2018-01-28

External link:
Download fulltext
Preprint
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)