2026-04-14T19:50:39Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/751372025-02-26T20:01:26Zcom_10324_32197com_10324_952com_10324_894col_10324_32199

Rodríguez Vítores, David Matrán Bea, Carlos 2025-02-26T12:50:58Z 2025-02-26T12:50:58Z 2024 Statistics and Computing, 2024, vol. 34, n. 4 0960-3174 https://uvadoc.uva.es/handle/10324/75137 10.1007/s11222-024-10410-y 3 Statistics and Computing 34 1573-1375 This work introduces a refinement of the Parsimonious Model for fitting a Gaussian Mixture. The improvement is based on the consideration of clusters of the involved covariance matrices according to a criterion, such as sharing Principal Directions. This and other similarity criteria that arise from the spectral decomposition of a matrix are the bases of the Parsimonious Model. We show that such groupings of covariance matrices can be achieved through simple modifications of the CEM (Classification Expectation Maximization) algorithm. Our approach leads to propose Gaussian Mixture Models for model- based clustering and discriminant analysis, in which covariance matrices are clustered according to a parsimonious criterion, creating intermediate steps between the fourteen widely known parsimonious models. The added versatility not only allows us to obtain models with fewer parameters for fitting the data, but also provides greater interpretability. We show its usefulness for model-based clustering and discriminant analysis, providing algorithms to find approximate solutions verifying suitable size, shape and orientation constraints, and applying them to both simulation and real data examples. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2024 The Author(s) Atribución 4.0 Internacional Improving model choice in classification: an approach based on clustering of covariance matrices info:eu-repo/semantics/article