Hi David,
Doesn't the fact that the alpha parameters are not learned make the
algorithm very dependent on the initial number of topics that is provided by
the user (k <numTopics>), i.e. it could learn how the words are distributed
by topics, but it cannot learn the correct number of topics. May be approach
similar to the one implemented in the Dirichlet algorithm could be used,
which has initial prior alpha and then the number of "meaningful" topics is
refined depending on how many words each topic has collected (i.e. the less
words a topic has attracted the less probable this topic becomes as whole).
Regards, Vasil
On Thu, Mar 10, 2011 at 8:40 PM, David Hall <dlwh@cs.berkeley.edu> wrote:
> 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(wordtopic) (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 welldocumented.
> >>
> >> 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(wordtopic), 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
> >>
> >
>
