On the slow convergence of EM and VBEM in low-noise linear models

Kaare Brandt Petersen; Ole Winther; Lars Kai Hansen

doi:10.1162/0899766054322991

On the slow convergence of EM and VBEM in low-noise linear models

Kaare Brandt Petersen, Ole Winther, Lars Kai Hansen

25 Citationer (Scopus)

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.

Originalsprog	Engelsk
Tidsskrift	Neural Computation
Vol/bind	17
Udgave nummer	9
Sider (fra-til)	1921-1926
Antal sider	6
ISSN	0899-7667
DOI	https://doi.org/10.1162/0899766054322991
Status	Udgivet - sep. 2005
Udgivet eksternt	Ja

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10.1162/0899766054322991

Citationsformater

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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.",

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AU - Petersen, Kaare Brandt

AU - Winther, Ole

AU - Hansen, Lars Kai

PY - 2005/9

Y1 - 2005/9

N2 - 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.

AB - 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.

KW - Algorithms

KW - Artifacts

KW - Bayes Theorem

KW - Factor Analysis, Statistical

KW - Linear Models

KW - Principal Component Analysis

U2 - 10.1162/0899766054322991

DO - 10.1162/0899766054322991

M3 - Journal article

C2 - 16106590

SN - 0899-7667

VL - 17

SP - 1921

EP - 1926

JO - Neural Computation

JF - Neural Computation

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ER -

On the slow convergence of EM and VBEM in low-noise linear models

Abstract

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