Speaker
Bert Kappen
(Radboud University)
Description
In this talk, I present a new model and solution method for
sparse regression. The model introduces binary selector
variables $s_i$ for the features $i$ in a way that is similar
to the original garrote model. The posterior probability for
$s_i$ is computed in the variational approximation. I refer
to this method as the Variational Garrote (VG). The VG is
compared numerically with the Lasso method and with
ridge regression. Numerical results on synthetic data show
that the VG yields more accurate predictions and more
accurately reconstructs the true model than the other
methods. The naive implementation of the VG requires the
inversion of a modified covariance matrix which scales cubic
in the number of features. We indicate how for sparse
problem the solution can be computed linear in the number
of features.
