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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