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Generative linear mixture modelling.

Lawson, Antony and Einbeck, Jochen (2012) 'Generative linear mixture modelling.', in 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings. Amsterdam: Statistical Modeling Society, pp. 595-600. Proceedings of the international workshop on statistical modelling., 2

Abstract

For multivariate data with a low–dimensional latent structure, a novel approach to linear dimension reduction based on Gaussian mixture models is pro- posed. A generative model is assumed for the data, where the mixture centres (or ‘mass points’) are positioned along lines or planes spanned through the data cloud. All involved parameters are estimated simultaneously through the EM al- gorithm, requiring an additional iteration within each M-step. Data points can be projected onto the low–dimensional space by taking the posterior mean over the estimated mass points. The compressed data can then be used for further pro- cessing, for instance as a low–dimensional predictor in a multivariate regression problem.

Item Type:Book chapter
Keywords:EM, Dimension reduction, Mixture modelling.
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://www.statmod.org/workshops_archive_proceedings_2012.html
Date accepted:No date available
Date deposited:19 June 2013
Date of first online publication:2012
Date first made open access:No date available

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