title: On the slow convergence of EM and VBEM in low-noise linear models.
abstract: We analyze convergence of the expectation maximization (EM) and variational Bayes EM (VBEM) schemes for parameter estimation in noisy linear models. The analysis shows that both schemes are inefficient in the low-noise limit. The linear model with additive noise includes as special cases independent component analysis, probabilistic principal component analysis, factor analysis, and Kalman filtering. Hence, the results are relevant for many practical applications.
authors.affiliation: Informatics and Mathematical Modeling, Technical University of Denmark, Building 321, DK = 2300 Kongens Lyngby, Denmark. kbp@imm.dtu.dk
authors.forename: Kaare Brandt
authors.initials: KB
authors.lastname: Petersen
authors.forename: Ole
authors.initials: O
authors.lastname: Winther
authors.forename: Lars Kai
authors.initials: LK
authors.lastname: Hansen
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D005163:Factor Analysis, Statistical; D016014:Linear Models; D025341:Principal Component Analysis