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SUMMARY:A generalized method for Sparse Partial Least Squares (Dual-SPLS):
theory and applications
DTSTART;VALUE=DATE-TIME:20210503T083000Z
DTEND;VALUE=DATE-TIME:20210503T093000Z
DTSTAMP;VALUE=DATE-TIME:20220116T212603Z
UID:indico-event-6595@indico.math.cnrs.fr
DESCRIPTION:In data analysis\, high dimensionality is often a delicate obs
tacle to overcome. The problem can be solved by representing the data in a
lower dimensional space using projection methods like the Partial Least S
quares regression (PLS) or by resorting to variable selection methods like
the lasso approach. The Sparse Partial Least Squares (SPLS) combines the
two approaches in order to better interpret the results due to the sparsit
y imposed on the new directions. Several implementations have been propose
d. However\, problems of accuracy of predictions and correct interpretatio
n of regression coefficients arise in these approaches. Hence we developed
the Dual Sparse Partial Least Squares\, a flexible method that results in
more accurate predictions and better interpretation of the coefficients d
ue to their sparsity. In this paper we present the theory behind Dual-SPLS
and some applicative results on petroleum data sets.\n\nhttps://indico.ma
th.cnrs.fr/event/6595/
LOCATION:Webinar BigBlueButton Platform
URL:https://indico.math.cnrs.fr/event/6595/
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