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From David Hall <d...@cs.berkeley.edu>
Subject Re: Question about LDA parameter estimation
Date Thu, 10 Mar 2011 18:40:17 GMT
err, Jae, sorry.

-- David

On Thu, Mar 10, 2011 at 10:33 AM, David Hall <dlwh@cs.berkeley.edu> wrote:
> 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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