An optimization-based algorithm for model selection using an approximation of Akaike's Information Criterion
Rodrigo Carvajal, Gabriel Urrutia, Juan C. Agüero · 2016
In this paper, we consider an optimization approach for model selection using Akaike's Information Criterion (AIC) by incorporating the ℓ0-(pseudo)norm as a penalty function to the log-likelihood function. In order to reduce the numerical complexity of the optimization problem, we propose to approximate the ℓ0-(pseudo)norm by an exponential term. We focus on problems with hidden variables- i.e. where there are random variables that we cannot measure, and the Expectation-Maximization (EM) algorithm. We illustrate the benefits of our proposal via numerical simulations.