The classification problem in the case that groups are known and both labeled and unlabeled data are available is analyzed. The classification rule is ERCIM WG on Computing & Statistics⃝c 99 ES07 Room B18 MIXTURE MODELS Chair: Christian Hennig Monday 19.12.2011 10:55 - 12:35 CFE-ERCIM 2011 Parallel Session N – ERCIM derived using Gaussian mixtures, with covariance matrices fixed according to a multiple testing procedure, which allows us to choose among four alternatives: heteroscedasticity, homometroscedasticity (common eigenvalue matrices between groups), homotroposcedasticity (common eigen- vector matrices between groups), and homoscedasticity. The mixture models are then fitted using either only the labeled data or all available ones (labeled and unlabeled) adopting the EM and the CEM algorithms in the latter case. Applications on real data are provided in order to show the classification performance of the proposed procedure.

Bagnato, L., Greselin, F., Ingrassia, S., Punzo, A. (2011). Normal discriminant analysis via the 2-terms eigenvalue decomposition. In Book of abstract 5th CSDA International Conference on Computational and Financial Econometrics (CFE 2011) and 4th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computing & Statistics (ERCIM 2011). London : ERCIM WG on Computing & Statistics⃝c.

Normal discriminant analysis via the 2-terms eigenvalue decomposition

GRESELIN, FRANCESCA;
2011

Abstract

The classification problem in the case that groups are known and both labeled and unlabeled data are available is analyzed. The classification rule is ERCIM WG on Computing & Statistics⃝c 99 ES07 Room B18 MIXTURE MODELS Chair: Christian Hennig Monday 19.12.2011 10:55 - 12:35 CFE-ERCIM 2011 Parallel Session N – ERCIM derived using Gaussian mixtures, with covariance matrices fixed according to a multiple testing procedure, which allows us to choose among four alternatives: heteroscedasticity, homometroscedasticity (common eigenvalue matrices between groups), homotroposcedasticity (common eigen- vector matrices between groups), and homoscedasticity. The mixture models are then fitted using either only the labeled data or all available ones (labeled and unlabeled) adopting the EM and the CEM algorithms in the latter case. Applications on real data are provided in order to show the classification performance of the proposed procedure.
paper
Mixture models; discriminant analysis; homoscedasticity; heteroscedasticity; patterned covariance matrices
English
4th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computing & Statistics (ERCIM 2011)
2011
Stan Azen, Ana Colubi, Erricos J. Kontoghiorghes, George Loizou and Irini Moustaki.
Book of abstract 5th CSDA International Conference on Computational and Financial Econometrics (CFE 2011) and 4th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computing & Statistics (ERCIM 2011)
1-dic-2011
http://www.cfe-csda.org/ercim11/London2011BoA.pdf
none
Bagnato, L., Greselin, F., Ingrassia, S., Punzo, A. (2011). Normal discriminant analysis via the 2-terms eigenvalue decomposition. In Book of abstract 5th CSDA International Conference on Computational and Financial Econometrics (CFE 2011) and 4th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computing & Statistics (ERCIM 2011). London : ERCIM WG on Computing & Statistics⃝c.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/33576
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