MclustSSC semi-supervised classification
Semi-Supervised classification based on Gaussian finite mixture modeling.
MclustSSC(data, class,
G = NULL, modelNames = NULL,
prior = NULL, control = emControl(),
warn = mclust.options("warn"),
verbose = interactive(),
...)data |
A data frame or matrix giving the training data. |
class |
A vector giving the known class labels (either a numerical value or
a character string) for the observations in the training data.
Observations with unknown class are encoded as |
G |
An integer value specifying the numbers of mixture components or classes. By default is set equal to the number of known classes. See the examples below. |
modelNames |
A vector of character strings indicating the models to be fitted
by EM (see the description in |
prior |
The default assumes no prior, but this argument allows specification of a
conjugate prior on the means and variances through the function
|
control |
A list of control parameters for EM. The defaults are set by the call
|
warn |
A logical value indicating whether or not certain warnings
(usually related to singularity) should be issued when
estimation fails.
The default is controlled by |
verbose |
A logical controlling if a text progress bar is displayed during the
fitting procedure. By default is |
... |
Further arguments passed to or from other methods. |
The semi-supervised approach implemented in MclustSSC() is a simple Gaussian mixture model for classification where at the first M-step only observations with known class labels are used for parameters estimation. Then, a standard EM algorithm is used for updating the probabiltiy of class membership for unlabelled data while keeping fixed the probabilities for labelled data.
An object of class 'MclustSSC' providing the optimal (according
to BIC) Gaussian mixture model for semi-supervised classification.
The details of the output components are as follows:
call |
The matched call. |
data |
The input data matrix. |
class |
The input class labels (including |
modelName |
A character string specifying the "best" estimated model. |
G |
A numerical value specifying the number of mixture components or classes of the "best" estimated model. |
n |
The total number of observations in the data. |
d |
The dimension of the data. |
BIC |
All BIC values. |
loglik |
Log-likelihood for the selected model. |
df |
Number of estimated parameters. |
bic |
Optimal BIC value. |
parameters |
A list with the following components:
|
z |
A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class. |
classification |
The classification corresponding to |
prior |
The prior used (if any). |
control |
A list of control parameters used in the EM algorithm. |
Luca Scrucca
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.
# Simulate two overlapping groups
n <- 200
pars <- list(pro = c(0.5, 0.5),
mean = matrix(c(-1,1), nrow = 2, ncol = 2, byrow = TRUE),
variance = mclustVariance("EII", d = 2, G = 2))
pars$variance$sigmasq <- 1
data <- sim("EII", parameters = pars, n = n, seed = 12)
class <- data[,1]
X <- data[,-1]
clPairs(X, class, symbols = c(1,2), main = "Full classified data")
# Randomly remove labels
cl <- class; cl[sample(1:n, size = 195)] <- NA
table(cl, useNA = "ifany")
clPairs(X, ifelse(is.na(cl), 0, class),
symbols = c(0, 16, 17), colors = c("grey", 4, 2),
main = "Partially classified data")
# Fit semi-supervised classification model
mod_SSC <- MclustSSC(X, cl)
summary(mod_SSC, parameters = TRUE)
pred_SSC <- predict(mod_SSC)
table(Predicted = pred_SSC$classification, Actual = class)
ngrid <- 50
xgrid <- seq(-3, 3, length.out = ngrid)
ygrid <- seq(-4, 4.5, length.out = ngrid)
xygrid <- expand.grid(xgrid, ygrid)
pred_SSC <- predict(mod_SSC, newdata = xygrid)
col <- mclust.options("classPlotColors")[class]
pch <- class
pch[!is.na(cl)] = ifelse(cl[!is.na(cl)] == 1, 19, 17)
plot(X, pch = pch, col = col)
contour(xgrid, ygrid, matrix(pred_SSC$z[,1], ngrid, ngrid),
add = TRUE, levels = 0.5, drawlabels = FALSE, lty = 2, lwd = 2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.