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topicmodels (version 0.0-6)

LDA: Latent Dirichlet Allocation

Description

Estimate a LDA model using the VEM algorithm or Gibbs Sampling.

Usage

LDA(x, k, method = c("VEM", "Gibbs"), control = NULL,
    model = NULL, ...)

Arguments

x
Object of class "DocumentTermMatrix"
k
Integer; number of topics
method
The method to be used for fitting; currently method = "VEM" or method= "Gibbs" are supported.
control
A named list of the control parameters for estimation or an object of class "LDAcontrol".
model
Object of class "LDA" for initialization.
...
Optional arguments. Currently not used.

Value

  • LDA() returns an object of class "LDA".

Details

The C code for LDA from David M. Blei is used to estimate and fit a latent dirichlet allocation model with the VEM algorithm.

For Gibbs Sampling the C++ code from Xuan-Hieu Phan is used.

References

Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993--1022.

Phan X.H., Nguyen L.M., Horguchi S. (2008). Learning to Classify Short and Sparse Text & Web with Hidden Topics from Large-scale Data Collections. In Proceedings of the 17th International World Wide Web Conference (WWW 2008), pages 91--100. Beijing, China.

See Also

"LDAcontrol"

Examples

Run this code
data("AssociatedPress", package = "topicmodels")
lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
lda_inf <- LDA(AssociatedPress[21:30,], model = lda,
               control = list(em = list(iter.max = -1L)))

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