Exponential family mixed membership models for soft clustering of multivariate data
|Title:||Exponential family mixed membership models for soft clustering of multivariate data||Authors:||White, Arthur
Murphy, Thomas Brendan
|Permanent link:||http://hdl.handle.net/10197/7981||Date:||Dec-2016||Abstract:||For several years, model-based clustering methods have successfully tackled many of the challenges presented by data-analysts. However, as the scope of data analysis has evolved, some problems may be beyond the standard mixture model framework. One such problem is when observations in a dataset come from overlapping clusters, whereby different clusters will possess similar parameters for multiple variables. In this setting, mixed membership models, a soft clustering approach whereby observations are not restricted to single cluster membership, have proved to be an effective tool. In this paper, a method for fitting mixed membership models to data generated by a member of an exponential family is outlined. The method is applied to count data obtained from an ultra running competition, and compared with a standard mixture model approach.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Springer||Copyright (published version):||2016 Springer-Verlag Berlin Heidelberg||Keywords:||Machine learning;Statistics;Mixed membership models;Model based clustering;Mixture models;Variational Bayes||DOI:||10.1007/s11634-016-0267-5||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Insight Research Collection
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