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6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

May 2015, Vol. 27, No. 5, Pages 1142-1157
(doi: 10.1162/NECO_a_00723)
© 2015 Massachusetts Institute of Technology
Online EM with Weight-Based Forgetting
Article PDF (546.72 KB)
Abstract

In the online version of the EM algorithm introduced by Sato and Ishii (2000), a time-dependent discount factor is introduced for forgetting the effect of the old estimated values obtained with an earlier, inaccurate estimator. In their approach, forgetting is uniformly applied to the estimators of each mixture component depending exclusively on time, irrespective of the weight attributed to each unit for the observed sample. This causes an excessive forgetting in the less frequently sampled regions. To address this problem, we propose a modification of the algorithm that involves a weight-dependent forgetting, different for each mixture component, in which old observations are forgotten according to the actual weight of the new samples used to replace older values. A comparison of the time-dependent versus the weight-dependent approach shows that the latter improves the accuracy of the approximation and exhibits much greater stability.