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(Neural Computation. 2005;17:1921-1926.)
© 2005 The MIT Press

Note

On the Slow Convergence of EM and VBEM in Low-Noise Linear Models

Kaare Brandt Petersen

kbp{at}imm.dtu.dk, Informatics and Mathematical Modeling, Technical University of Denmark, Building 321, DK = 2300 Kongens Lyngby, Denmark

Ole Winther

owi{at}imm.dtu.dk, Informatics and Mathematical Modeling, Technical University of Denmark, Building 321, DK = 2300 Kongens Lyngby, Denmark

Lars Kai Hansen

lkhansen{at}imm.dtu.dk, Informatics and Mathematical Modeling, Technical University of Denmark, Building 321, DK = 2300 Kongens Lyngby, Denmark

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.






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J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 2005 by The MIT Press.