gmm11R {T4cluster} | R Documentation |
Regularized GMM by Ruan et al. (2011)
Description
Ruan et al. (2011) proposed a regularized covariance estimation by
graphical lasso to cope with high-dimensional scenario where conventional
GMM might incur singular covariance components. Authors proposed to use
\lambda
as a regularization parameter as normally used in
sparse covariance/precision estimation problems and suggested to use the
model with the smallest BIC values.
Usage
gmm11R(data, k = 2, lambda = 1, ...)
Arguments
data |
an |
k |
the number of clusters (default: 2). |
lambda |
regularization parameter for graphical lasso (default: 1). |
... |
extra parameters including
|
Value
a named list of S3 class T4cluster
containing
- cluster
a length-
n
vector of class labels (from1:k
).- mean
a
(k\times p)
matrix where each row is a class mean.- variance
a
(p\times p\times k)
array where each slice is a class covariance.- weight
a length-
k
vector of class weights that sum to 1.- loglkd
log-likelihood of the data for the fitted model.
- algorithm
name of the algorithm.
References
Ruan L, Yuan M, Zou H (2011). “Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models.” Neural Computation, 23(6), 1605–1622. ISSN 0899-7667, 1530-888X.
Examples
# -------------------------------------------------------------
# clustering with 'iris' dataset
# -------------------------------------------------------------
## PREPARE
data(iris)
X = as.matrix(iris[,1:4])
lab = as.integer(as.factor(iris[,5]))
## EMBEDDING WITH PCA
X2d = Rdimtools::do.pca(X, ndim=2)$Y
## COMPARE WITH STANDARD GMM
cl.gmm = gmm(X, k=3)$cluster
cl.11Rf = gmm11R(X, k=3)$cluster
cl.11Rd = gmm11R(X, k=3, usediag=TRUE)$cluster
## VISUALIZATION
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3), pty="s")
plot(X2d, col=cl.gmm, pch=19, main="standard GMM")
plot(X2d, col=cl.11Rf, pch=19, main="gmm11R: full covs")
plot(X2d, col=cl.11Rd, pch=19, main="gmm11R: diagonal covs")
par(opar)