The LDA function uses training data and parameters to build a topic model, using an unsupervised method to estimate the correlation between the topics and words according to the topic number and other parameters. Optionally, the function creates the topic distributions for each training document.
The function uses an iterative algorithm; therefore, applying it to large data sets with a large number of topics can be time-consuming.
The function assumes that the model table can be fitted into the memory of the vworkers.