  Mixtures-of-Experts models and their maximum likelihood estimation (MLE) via
the EM algorithm have been thoroughly studied in the statistics and machine
learning literature. They are subject of a growing investigation in the context
of modeling with high-dimensional predictors with regularized MLE. We examine
MoE with Gaussian gating network, for clustering and regression, and propose an
$\ell_1$-regularized MLE to encourage sparse models and deal with the
high-dimensional setting. We develop an EM-Lasso algorithm to perform parameter
estimation and utilize a BIC-like criterion to select the model parameters,
including the sparsity tuning hyperparameters. Experiments conducted on
simulated data show the good performance of the proposed regularized MLE
compared to the standard MLE with the EM algorithm.
