Thanks for your feedback.
On 03/25/2015 02:59 PM, Wei Xue wrote:
Thanks Andreas, Kyle, Vlad and Olivier for the detailed review.
1. For the part /Implementing VBGMM, /do you mean it would be better
if I add specific functions to be implemented? @Andreas.
I just felt the paragraph was a bit unclear, and would benefit from
saying what exactly you want to do.
6. I would like to add a variance of EM estimation to GMM module, MAP
estimation. Currently, the m-step use maximum likelihood estimation
with min_covariance which prevent singular covariance estimation. I
think it would be better to add MAP estimation for m-step, because the
fixed min_covariance in ML estimation might be too aggressive in some
cases. In MAP, the effect of correcting covariance will be decreasing
as the number of data instances increases.
How is this different from the VBGMM?
7. I would also like to add some functionality to deal with missing
values in GMM. The situation with missing value in the training data
is not uncommon and PRML book also mentioned that.
I think this is outside the scope of this project, as we generally have
avoided dealing with missing values in sklearn estimators directly.
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