Abstract
In this contribution we deal with the problem of learning an undi-
rected graph which encodes the conditional dependence relationship be-
tween variables of a complex system, given a set of observations of this
system. This is a very central problem of modern data analysis and it
comes out every time we want to investigate a deeper relationship be-
tween random variables, which is different from the classical dependence
usually measured by the covariance.
In particular, in this contribution we deal with the case of Gaussian
Graphical Models (GGMs) for which the system of variables has a mul-
tivariate gaussian distribution. We revise some of the existing techniques
for such a problem and propose a smart implementation of the symmetric
parallel regression technique which turns out to be very competitive for
learning sparse GGMs under high dimensional data regime.
Anno
2018
Autori IAC
Tipo pubblicazione
Altri Autori
De Canditiis, Daniela and Guardasole Armando