This work deals with the classification problem in the case that groups are known and both labeled and unlabeled data are available. The classification rule is derived using Gaussian mixtures where covariance matrices are given according to a multiple testing procedure which asesses a pattern among heteroscedasticity, homometroscedasticity, homotroposcedasticity, and homoscedasticity. The mixture models are then fitted using all available data (labeled and unlabeled) and adopting the EM and the CEM algorithms. The performance of the proposed procedure is evaluated by a simulation study.
|Title of host publication||Statistical Models for Data Analysis|
|Editors||PAOLO GIUDICI, SALVATORE INGRASSIA, MAURIZIO VICHI|
|Number of pages||10|
|Publication status||Published - 2013|
- Discriminant analysis