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From David Hall <d...@cs.berkeley.edu>
Subject Re: Question about LDA parameter estimation
Date Thu, 10 Mar 2011 18:33:44 GMT
Hi Bae,

We only try to obtain MLE's of p(word|topic) (beta), and we treat
alpha and eta as fixed. As you say, those could be learned, and it
might improve performance, but it's just not implemented.

There's no particular reason they're not implemented, but they're not
critical to getting basic LDA working, especially MAP estimation of
\beta.

-- David

On Wed, Mar 9, 2011 at 10:28 PM, Bae, Jae Hyeon <metacret@gmail.com> wrote:
> Hi
>
> I am studying LDA algorithm for my statistics project. The goal is fully
> understanding LDA algorithms and statistical concepts behind that and
> analyze implementation. I've chosen Mahout LDA implementation because it's
> scalable and well-documented.
>
> According to the original paper written by Blei, Ng, Jordan,
> parameters(alpha, beta) would be estimated with variational EM method. But I
> can't find any numerical methods to optimize those parameters. In Mahout
> implementation, alpha is topic smoothing input by user, beta is just
> P(word|topic), not estimated.
>
> I think that this implementation has a basic assumption. I want to know
> whether there was specific reason to implement like this without parameter
> estimation.
>
> Thank you
>
> Best, Jay
>

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