On 11 February 2015 at 23:21, Gael Varoquaux <gael.varoqu...@normalesup.org>
wrote:
> On Wed, Feb 11, 2015 at 03:55:12PM -0700, Anirudh Acharya wrote:
> > Is the following a good idea for GSoC 2015.
>
> > * Latent Dirichlet Allocation using Markov Chain Monte Carlo
> > * Extend to do inference with online stream of documents.
>
> MCMC no. We ruled against it, as MCMC require techniques that are not
> used very much in scikit-learn. But there is a pull request implementing
> the online non MCMC Latent Dirichlet Allocation algorithm.
>
> Gaël
>
If not MCMC, could we try other approximate inference techniques like
variational bayes, which are comparatively faster. Wouldn't having LDA as
part of scikit-learn would be good as LDA is also a way of looking at topic
models as a Bayesian Matrix Factorization approach for Sparse Matrices.
https://sites.google.com/site/igorcarron2/matrixfactorizations
http://www.wsdm-conference.org/2010/proceedings/docs/p91.pdf
http://www.cs.cmu.edu/~ggordon/singh-gordon-unified-factorization-ecml.pdf
-
Anirudh
>
>
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--
Anirudh Acharya
Graduate Student
Arizona State University
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