On Convergence Properties of the EM Algorithm for Gaussian Mixtures

dc.creator	Jordan, Michael
dc.creator	Xu, Lei
dc.date	2004-10-20T20:49:25Z
dc.date	2004-10-20T20:49:25Z
dc.date	1995-04-21
dc.date.accessioned	2013-10-09T02:48:31Z
dc.date.available	2013-10-09T02:48:31Z
dc.date.issued	2013-10-09
dc.identifier	AIM-1520
dc.identifier	CBCL-111
dc.identifier	http://hdl.handle.net/1721.1/7195
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	"Expectation-Maximization'' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix $P$, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of $P$ and provide new results analyzing the effect that $P$ has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models.
dc.format	9 p.
dc.format	291671 bytes
dc.format	476864 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-1520
dc.relation	CBCL-111
dc.subject	learning
dc.subject	neural networks
dc.subject	EM algorithm
dc.subject	clustering
dc.subject	mixture models
dc.subject	statistics
dc.title	On Convergence Properties of the EM Algorithm for Gaussian Mixtures

Files in this item

Files	Size	Format	View
There are no files associated with this item.