Article (Scientific journals)
HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data
Berge, Laurent; Bouveyron, Charles; Girard, Stéphane
2012In Journal of Statistical Software, 46 (6)
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Abstract :
[en] This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this parametrization is called high dimensional discriminant analysis (HDDA). In a similar manner, the associated clustering method is called high dimensional data clustering (HDDC) and uses the expectation-maximization algorithm for inference. In order to correctly fit the data, both methods estimate the specific subspace and the intrinsic dimension of the groups. Due to the constraints on the covariance matrices, the number of parameters to estimate is significantly lower than other model-based methods and this allows the methods to be stable and efficient in high dimensions. Two introductory examples illustrated with R codes allow the user to discover the hdda and hddc functions. Experiments on simulated and real datasets also compare HDDC and HDDA with existing classification methods on high-dimensional datasets. HDclassif is a free software and distributed under the general public license, as part of the R software project.
Disciplines :
Computer science
Author, co-author :
Berge, Laurent ;  University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Center for Research in Economic Analysis (CREA)
Bouveyron, Charles
Girard, Stéphane
External co-authors :
yes
Language :
English
Title :
HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data
Publication date :
2012
Journal title :
Journal of Statistical Software
ISSN :
1548-7660
Publisher :
University of California at Los Angeles, Los Angeles, United States - California
Volume :
46
Issue :
6
Peer reviewed :
Peer Reviewed verified by ORBi
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