Hi Wei Xue.
I am also not very convinced by the core-set approach.
I'd rather focus on improving the API and fixing issues in the VBGMM and
DPGMM.
I was hoping that Murphy's book has some more details on DPGMM, but I
didn't find any yet. He doesn't seem to talk about variational inference
in Dirichlet processes.
So far I think your proposal looks solid.
It would be great if you could work on some pull requests to support
your application.
Best,
Andy
On 03/16/2015 04:23 PM, Wei Xue wrote:
Hi groups,
I am a PhD student in Florida International University, US. I am
interested in the topic improving GMM. I draft a proposal for this topic.
https://github.com/xuewei4d/scikit-learn/wiki/GSoC-2015-Proposal:-Improve-GMM
Here are some questions I would like to discuss.
1. -1 for coreset. The
paper(http://las.ethz.ch/files/feldman11scalable-long.pdf) is new and
its citations less than 15. The application situations are on
clusters, streaming data, which is (I think) is rare for scikit-learn.
2. Currently, I have gone over the Approximation Inference chapter in
PRML (Bishop's machine learning book) and Blei's 2006 paper. But I
have not dig much into the code, so I don't have a detailed
reimplement plan yet. Do I need to add more details into the 'Theory
and Implementation' part of the proposal?
3. Any feedback is welcome.
Thanks,
Wei Xue
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