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Generative linear mixture modelling

Lawson, Antony; Einbeck, Jochen

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Authors

Antony Lawson



Contributors

Arnost Komarek
Editor

Stanislav Nagy
Editor

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.

Citation

Lawson, A., & Einbeck, J. (2012). Generative linear mixture modelling. In A. Komarek, & S. Nagy (Eds.), 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings (595-600)

Conference Name International workshop on statistical modelling.
Conference Location Prague
Publication Date Jan 1, 2012
Deposit Date Sep 24, 2012
Publicly Available Date Mar 29, 2024
Volume 2
Pages 595-600
Series Title Proceedings of the international workshop on statistical modelling.
Book Title 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings.
Keywords EM, Dimension reduction, Mixture modelling.
Public URL https://durham-repository.worktribe.com/output/1157680
Publisher URL http://www.statmod.org/workshops_archive_proceedings_2012.html
Additional Information http://www.maths.dur.ac.uk/~dma0je/Papers/lawson_einbeck_iwsm2012.pdf

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