# Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models

arXiv: https://arxiv.org/abs/1909.05494 · HTML: https://mlsanity.com/paper/1909.05494/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts.
- First posted on arXiv 12 Sep 2019.

## Placed in

- Mixture-of-experts / [MoE theory and gating functions](https://mlsanity.com/moe/moe-theory-and-gating-functions/)

## Built on by (most cited first, 3 of 3)

- [Mixture of Experts (MoE): A Big Data Perspective](https://mlsanity.com/paper/2501.16352/) (2025)
- [Non-asymptotic model selection in block-diagonal mixture of polynomial experts models](https://mlsanity.com/paper/2104.08959/) (2021)
- [Functional Mixtures-of-Experts](https://mlsanity.com/paper/2202.02249/) (2022)

## Abstract (arXiv)

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 ℓ1-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.