Algorithme EM régularisé
Pierre Houdouin Matthieu Jonkcheere Frederic Pascal
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm
for computing maximum likelihood estimate when dealing with Gaussian Mixture
Model (GMM). When the sample size is smaller than the data dimension, this
could lead to a singular or poorly conditioned covariance matrix and, thus, to
performance reduction. This paper presents a regularized version of the EM
algorithm that efficiently uses prior knowledge to cope with a small sample
size. This method aims to maximize a penalized GMM likelihood where regularized
estimation may ensure positive definiteness of covariance matrix updates by
shrinking the estimators towards some structured target covariance matrices.
Finally, experiments on real data highlight the good performance of the
proposed algorithm for clustering purposes