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My objective is to implement a topic model for a large number of documents (20M or 30M). Let us assume that the number of topics is fixed at 50.

I think implementing an LDA for the above problem would not be difficult. However, I have yet to find an answer for an NMF model. I have read that it is NOT easy to implement an NMF model for a large number of documents.

Is it really not possible to implement an NMF model for my problem?

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    $\begingroup$ I suppose NMF=non-negative matrix factorisation and LDA=latent Dirichlet allocation? It is usually a good idea to explain what abbreviations mean. $\endgroup$
    – Momo
    Oct 11, 2012 at 11:05
  • $\begingroup$ Thanks a lot, Momo. Any suggestions? $\endgroup$ Oct 11, 2012 at 17:31
  • $\begingroup$ I don't know the answer, sorry. Maybe try something distributed like research.microsoft.com/pubs/119077/DNMF.pdf $\endgroup$
    – Momo
    Oct 11, 2012 at 19:20

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Note on implementing LDA for this problem: there are well-designed inference algorithms for huge numbers of documents. Specifically, you should check out "Online LDA", which can adaptively train the topics looking at small chunks of documents at a time.

Paper: http://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf

Matt Hoffman has python code available: http://www.cs.princeton.edu/~blei/topicmodeling.html

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