Regularised maximum-likelihood inference of mixture of experts for regression and clustering

International audienceVariable selection is fundamental to high-dimensional statistical modeling, and is challenging in particular in unsupervised mod-eling, including mixture models. We propose a regularised maximum-likelihood inference of the Mixture of Experts model which is able to deal with potentially correlated features and encourages sparse models in a potentially high-dimensional scenarios. We develop a hybrid Expectation-Majorization-Maximization (EM/MM) algorithm for model fitting. Unlike state-of-the art regularised ML inference [1, 2], the proposed modeling doesn't require an approximate of the regularisation. The proposed algorithm allows to automatically obtain sparse solutions without thresholding, and includes coordinate descent updates avoiding matrix inversion. An experimental study shows the capability of the algorithm to retrieve sparse solutions and for model fitting in model-based clustering of regression data