Description Usage Arguments Details Value Fields Author(s) References Examples
sslGmmEM
implements Gaussian Mixture Model with an EM algorithm,
and weights the unlabeled data by introducing lambda-EM technique.
1 | sslGmmEM(xl, yl, xu, seed = 0, improvement = 1e-04, p = 0.3)
|
xl |
a n * p matrix or data.frame of labeled data |
yl |
a n * 1 integer vector of labels. |
xu |
a m * p matrix or data.frame of unlabeled data |
seed |
an integer specifying random number generation state for spliting labeled data into training set and cross-validation set. |
improvement |
numeric. Minimal allowed improvement of parameters. |
p |
percentage of labeled data are splitted into cross-validation set. |
sslGmmEM
introduces unlabeled data into parameter estimation process. The weight lambda
is chosen by cross-validation.
The Gaussian Mixture Model is estimated based on maximum log likelihood function with an EM algorithm. The E-step
computes the probabilities of each class for every observation. The M-step computes parameters based on probabilities
obtained in the E-step.
a list of values is returned:
para
a numeric estimated parameter matrix in which the column represents variables and the row represents estimated means and standard deviation of each class. for example, the first and second row represents the mean and standard deviation of the first class, the third and fourth row represents the mean and standard deviation of the second class,etc.
classProb
the estimated class probabilities
yu
the predicted label of unlabeled data
optLambda
the optimal lambda chosen by cross-validation
Junxiang Wang
Kamal Nigam, Andrew Mccallum, Sebastian Thrun, Tom Mitchell(1999) Text Classification from Labeled and Unlabeled Documents using EM
1 2 3 4 5 6 7 8 9 10 | data(iris)
xl<-iris[,-5]
#Suppose we know the first twenty observations of each class
#and we want to predict the remaining with Gaussian Mixture Model
#1 setosa, 2 versicolor, 3 virginica
yl<-rep(1:3,each=20)
known.label <-c(1:20,51:70,101:120)
xu<-xl[-known.label,]
xl<-xl[known.label,]
l<-sslGmmEM(xl,yl,xu)
|
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