On the slow convergence of EM and VBEM in low-noise linear models.
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.
Petersen
Kaare Brandt
KB
Informatics and Mathematical Modeling, Technical University of Denmark, Building 321, DK = 2300 Kongens Lyngby, Denmark. kbp@imm.dtu.dk
Winther
Ole
O
Hansen
Lars Kai
LK
D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D005163:Factor Analysis, Statistical; D016014:Linear Models; D025341:Principal Component Analysis