Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

September 1, 2005, Vol. 17, No. 9, Pages 1921-1926
(doi: 10.1162/0899766054322991)
© 2005 Massachusetts Institute of Technology
On the Slow Convergence of EM and VBEM in Low-Noise Linear Models
Article PDF (76.17 KB)
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.