perplexity.bounds: Compute the lower bound of the perplexity of a topic model...

In kshirley/LDAtools: Tools to fit a topic model using Latent Dirichlet Allocation (LDA)

Description

This is an 'experimental' function that computes the lower bound of the perplexity of the training data in an LDA topic model. We claim that the perplexity of the training data is minimized when alpha and beta, the priors for the document-topic distributions and the topic-term distributions, respectively, approach zero, and the number of topics is equal to min(D, W), where D is the number of training documents and W is the size of the vocabulary. I'll have to write up the idea some day.

Usage

perplexity.bounds(term.id = integer(), alpha = double(), beta = double(),
  term.frequency = integer(), doc.frequency = integer(), print = 50)

Arguments

term.id	an integer vector containing the term ID number of every token in the corpus. Should take values between 1 and W, where W is the number of terms in the vocabulary.
alpha	the dirichlet prior parameter for the document-topic multionomial distributions
beta	the dirichlet prior parameter for the topic-term multionomial distributions
term.frequency	an integer vector containing the counts of the number of occurences of each term in the vocabulary
doc.frequency	an integer vector containing the number of tokens per document, whose length is equal to the total number of documents in the corpus.

Value

bounds a numeric vector of length two containing the upper and lower bound of perplexity. The lower bound is computed by the C function. The upper bound is simple – it is just the perplexity of a 1-topic model. Included as output just for convenience.

kshirley/LDAtools documentation built on May 20, 2019, 7:03 p.m.