An online Minorization-Maximization algorithm

International audienceModern statistical and machine learning settings often involve high data volume and data streaming, which require the development of online estimation algorithms. The online Expectation-Maximization (EM) algorithm extends the popular EM algorithm to this setting, via a stochastic approximation approach. We show that an online version of the Minorization-Maximization (MM) algorithm, which includes the online EM algorithm as a special case, can also be constructed in a similar manner. We demonstrate our approach via an application to the logistic regression problem and compare it to existing methods