Latent Semantic Analysis model
Creates LSA(Latent semantic analysis) model. See https://en.wikipedia.org/wiki/Latent_semantic_analysis for details.
LatentSemanticAnalysis LSA
R6Class
object.
For usage details see Methods, Arguments and Examples sections.
lsa = LatentSemanticAnalysis$new(n_topics) lsa$fit_transform(x, ...) lsa$transform(x, ...) lsa$components
$new(n_topics)
create LSA model with n_topics
latent topics
$fit_transform(x, ...)
fit model to an input sparse matrix (preferably in dgCMatrix
format) and then transform x
to latent space
$transform(x, ...)
transform new data x
to latent space
A LSA
object.
An input document-term matrix. Preferably in dgCMatrix
format
integer
desired number of latent topics.
Arguments to internal functions. Notably useful for fit_transform()
-
these arguments will be passed to rsparse::soft_svd
data("movie_review") N = 100 tokens = word_tokenizer(tolower(movie_review$review[1:N])) dtm = create_dtm(itoken(tokens), hash_vectorizer(2**10)) n_topics = 5 lsa_1 = LatentSemanticAnalysis$new(n_topics) d1 = lsa_1$fit_transform(dtm) # the same, but wrapped with S3 methods d2 = fit_transform(dtm, lsa_1)
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