Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
This work addresses feature selection and regularization in clustering and regression for high-dimensional data, representing an incremental improvement in the field.
The paper tackles the problem of high-dimensional predictors in Mixtures-of-Experts models by proposing an ℓ₁-regularized maximum likelihood estimation to encourage sparsity, with experiments on simulated data showing improved performance over standard methods.
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.