Laurent just sent me the following:
I have a late internship proposal at IFP Energies nouvelles. I would be delighted if you could advertise it. The update page is:
and the pdf file is here:
A text (same as the webpage if you need html code):
Sparse regression and dimension reduction for sensor measurements and data normalization
The instrumental context is that of multiple 1D data or measurements ym related to the the same phenomenon x, corrupted by random effects nm and a different scaling parameter am, due to uncontrolled sensor calibrations or measurement variability. The model is thus:
ym(k) = am x(k) + nm(k) .
The aim of the internship is to robustly estimate scaling parameters am (with confidence bounds) in the presence of missing data or outliers for potentially small, real-life signals x with large amplitude variations. The estimation should be as automatized as possible, based on data properties and priors (e.g. sparsity, positivity), so as to be used by non-expert users. Signals under study are for instance: vibration, analytical chemistry or biological data. Of particular interest for this internship is the study and performance assessment of robust loss or penalty functions (around the l2,1-norm) such as the R1-PCA or low-rank decomposition.
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