Hi Luca.
Have you had a look at the top of
https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-%28GSOC%29-2015
?
For an application, it is expected that you submit some patches to the
repo to get familiar with the codebase.
What is your github handle (I might have overlooked it).
Also, it would be great if you could push your code to github if you
think people might be interested.
Best,
Andy
On 03/06/2015 07:57 PM, Luca Puggini wrote:
Thanks a lot for the material provided on randomized pca and random
forest it would for sure help me in my research.
I talked with my supervisor and he said that I am free to apply for
this summer project.
I used quiet a lot GAM and I did some work related to high dimensional
fault detection system and so to metrics but apparently these topics
are already taken.
My understanding from the previous emails is that nipals PCA may be of
interest. On the same topic I have a couple of algorithms that I think
could be useful.
1- Sparse principal component analysis via regularized low rank matrix
approximation.
http://www.sciencedirect.com/science/article/pii/S0047259X07000887
This is basically the equivalent of the nipals algorithm for SPCA. It
is more efficient for high dimensional problem. It is pretty useful
because it is possible to avoid the initial SVD.
2- Feature Subset Selection and Ranking for Data Dimensionality
Reduction http://eprints.whiterose.ac.uk/1947/1/weihl3.pdf .
This is a method to do unsupervised features selection. Similar to
SPCA but it is optimized in order to maximize the percentage of
explained variance respect to the number of selected variables.
If these topics are not of interest I will be happy to work on
- improve GMM or -Global optimization based Hyperparameter optimization
I am not familiar with these 2 topics but they are close to my
research area so I will be happy to study them.
Now my understanding is that the staff should contact me to discuss
further the various arguments. Please fill free to contact me to my
private email and I am happy to share my cv and my python code
(research quality code )
Thanks a lot,
Luca
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