Refereed full papers (journals, book chapters, international conferences)

1998

@inbook{NeH1998,
	vgclass =	{refpap},
	author =	{Radford M. Neal and Geoffrey E. Hinton},
	title =	{A view of the {EM} algorithm that justifies incremental,
	sparse, and other variants},
	editor =	{Michael I. Jordan},
	booktitle =	{Learning in Graphical Models},
	pages =	{355--368},
	publisher =	{Kluwer Academic Publishers},
	year =	{1998},
	url =	{ftp://ftp.cs.utoronto.ca/pub/radford/emk.pdf},
	abstract =	{The EM algorithm performs maximum likelihood estimation
	for data in which some variables are unobserved. We present a function
	that resembles negative free energy and show that the M step maximizes
	this function with respect to the model parameters and the E step
	maximizes it with respect to the distribution over the unobserved
	variables. From this perspective, it is easy to justify an incremental
	variant of the EM algorithm in which the distribution for only one of
	the unobserved variables is recalculated in each E step. This variant
	is shown empirically to give faster convergence in a mixture estimation
	problem. A variant of the algorithm that exploits sparse conditional
	distributions is also described, and a wide range of other variant
	algorithms are also seen to be possible.},
}