The Fisher-EM algorithm

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Description

The Fisher-EM algorithm is a subspace clustering method for high-dimensional data. It is based on the Gaussian Mixture Model and on the idea that the data lives in a common and low dimensional subspace. An EM-like algorithm estimates both the discriminative subspace and the parameters of the mixture model.

Usage

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fem(Y,K=2:6,model='AkjB',method='reg',crit='icl',maxit=50,eps=1e-6,init='kmeans',
	nstart=25,Tinit=c(),kernel='',disp=F)

Arguments

Y

The data matrix. Categorical variables and missing values are not allowed.

K

An integer vector specifying the numbers of mixture components (clusters) among which the model selection criterion will choose the most appropriate number of groups. Default is 2:6.

model

A vector of discriminative latent mixture (DLM) models to fit. There are 12 different models: "DkBk", "DkB", "DBk", "DB", "AkjBk", "AkjB", "AkBk", "AkBk", "AjBk", "AjB", "ABk", "AB". The option "all" executes the Fisher-EM algorithm on the 12 DLM models and select the best model according to the maximum value obtained by model selection criterion.

method

The method use for the fitting of the projection matrix associated to the discriminative subspace. Three methods are available: 'svd', 'reg' and 'gs'. The 'reg' method is the default.

crit

The model selection criterion to use for selecting the most appropriate model for the data. There are 3 possibilities: "bic", "aic" or "icl". Default is "icl".

maxit

The maximum number of iterations before the stop of the Fisher-EM algorithm.

eps

The threshold value for the likelihood differences to stop the Fisher-EM algorithm.

init

The initialization method for the Fisher-EM algorithm. There are 4 options: "random" for a randomized initialization, "kmeans" for an initialization by the kmeans algorithm, "hclust" for hierarchical clustering initialization or "user" for a specific initialization through the parameter "Tinit". Default is "kmeans". Notice that for "kmeans" and "random", several initializations are asked and the initialization associated with the highest likelihood is kept (see "nstart").

nstart

The number of restart if the initialization is "kmeans" or "random". In such a case, the initialization associated with the highest likelihood is kept.

Tinit

A n x K matrix which contains posterior probabilities for initializing the algorithm (each line corresponds to an individual).

kernel

It enables to deal with the n < p problem. By default, no kernel (" ") is used. But the user has the choice between 3 options for the kernel: "linear", "sigmoid" or "rbf".

disp

If true, some messages are printed during the clustering. Default is false.

Value

A list is returned:

K

The number of groups.

cls

the group membership of each individual estimated by the Fisher-EM algorithm.

P

the posterior probabilities of each individual for each group.

U

The loading matrix which determines the orientation of the discriminative subspace.

mean

The estimated mean in the subspace.

my

The estimated mean in the observation space.

prop

The estimated mixture proportion.

D

The covariance matrices in the subspace.

aic

The value of the Akaike information criterion.

bic

The value of the Bayesian information criterion.

icl

The value of the integrated completed likelihood criterion.

loglik

The log-likelihood values computed at each iteration of the FEM algorithm.

ll

the log-likelihood value obtained at the last iteration of the FEM algorithm.

method

The method used.

call

The call of the function.

plot

Some information to pass to the plot.fem function.

crit

The model selction criterion used.

Author(s)

Charles Bouveyron and Camille Brunet

References

Charles Bouveyron and Camille Brunet (2012), Simultaneous model-based clustering and visualization in the Fisher discriminative subspace, Statistics and Computing, 22(1), 301-324.

Charles Bouveyron and Camille Brunet (2013), "Discriminative variable selection for clustering with the sparse Fisher-EM algorithm", Computational Statistics, to appear.

See Also

sfem, plot.fem, fem.ari, summary.fem

Examples

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data(iris)
res = fem(iris[,-5],K=2:5,model='AkB')
summary(res)
plot(res)
fem.ari(res,as.numeric(iris[,5]))