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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.