I have a more theoretical question about LDA (Latent Dirichet Allocation).
When doing LDA we provide number of topics ourselves. As far as I understand it tries to build topic-word-document distributions to minimize perplexity (which is why we are doing it in iterative manner).
So the question - can we fit the LDA several times with different number of topics, check the perplexity of each result and choose the number of topics which yielded minimal perplexity? Or am I misunderstanding the perplexity meaning and the algorithm itself - so the perplexity is actually not a 'fit measure' for LDA?