What is Latent Dirichlet Allocation?
In a general view, LDA is an unsupervised method for clustering documents. It models (purified) documents as bag of words. Also it assumes each word (and document) has a mixture model of topics i.e. each word (and document) may belongs to each of the topics by a probability. It takes number of clusters in the corpus as input then, simply assigns each word in each document a random topic. Then tries for
It was a very general description of LDA.
After dealing with the old site, I’ve decided to reform my blog to this site. 🙂
The old UUTElgg is closed.