Title: An online Minorization-Maximization algorithm
Abstract: Modern 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.
Keywords: Expectation–Maximization, Minorization–Maximization, parameter estimation, online algorithms, Stochastic Approximation