Hi Luca.
If you give write comment permissions, I could comment on the google doc
in-place which might be helpful.
As I think was commented earlier, the current PLS already implements
NIPALS. What would the addition be?
Use that in PCA? That is not super clear from the proposal.
I think implementing this together with the other paper you mention will
take more than one or two weeks.
Please keep in mind that it needs tests, documentation, examples and
reviews.
The "massive parallel" paper only has 8 citations since 2013. That seems
pretty low impact and not very established.
Unsupervised Feature Selection Using Feature Similarity seems a much
safer bet (800 cites since 2002), though I am not
familiar enough with the area to say if it is still comparable to state
of the art or useful.
Feature Subset Selection and Ranking for Data Dimensionality Reduction
seems borderline with 120 cites since 2007.
I haven't actually had time to check the papers (yet?), this is just a
first very superficial review.
Instead of focusing on many algorithms, I think you should also allocate
some time to ensure that we have good evaluation metrics and
cross-validation support
for multi-output algorithms where Y might be an input to transform (not
sure for how many of these algorithms this is the case).
How is the multi-task lasso that you are proposing different from the
one implemented already in scikit-learn?
http://scikit-learn.org/dev/modules/generated/sklearn.linear_model.MultiTaskLasso.html#sklearn.linear_model.MultiTaskLasso
The project sounds great, the hardest part might be finding the right
mentor (Gael?)
Cheers,
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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