Let's say I run an LDA model with 3 topics on 5 documents.
After the model is learned (with Gibbs sampling presumably), I have topic distribution for each document, shown as the following:
My question is, how do I retrieve document(s) that are "most similar to document-1" ?
In clustering algorithms such as K-means, each document is assigned to one of the K classes. To retrieve doc-1's neighbor documents, I just need to find all the documents that get assigned to the same cluster as doc-1.
What procedure should I do with LDA model?