Computational Aspects of Fitting Mixture Models via the Expectation-Maximization Algorithm
|Title:||Computational Aspects of Fitting Mixture Models via the Expectation-Maximization Algorithm||Authors:||O'Hagan, Adrian
Murphy, Thomas Brendan
Gormley, Isobel Claire
|Permanent link:||http://hdl.handle.net/10197/7110||Date:||Dec-2012||Abstract:||The Expectation–Maximization (EM) algorithm is a popular tool in a wide variety of statistical settings, in particular in the maximum likelihood estimation of parameters when clustering using mixture models. A serious pitfall is that in the case of a multimodal likelihood function the algorithm may become trapped at a local maximum, resulting in an inferior clustering solution. In addition, convergence to an optimal solution can be very slow. Methods are proposed to address these issues: optimizing starting values for the algorithm and targeting maximization steps efficiently. It is demonstrated that these approaches can produce superior outcomes to initialization via random starts or hierarchical clustering and that the rate of convergence to an optimal solution can be greatly improved.||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Computational Statistics and Data Analysis||Volume:||56||Issue:||12||Start page:||3843||End page:||3864||Copyright (published version):||2012 Elsevier||Keywords:||Convergence rate; Expectation–maximization algorithm; Hierarchical clustering; Model-based clustering; Multimodal likelihood||DOI:||10.1016/j.csda.2012.05.011||Language:||en||Status of Item:||Peer reviewed||metadata.dc.date.available:||2015-09-25T11:55:37Z|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.