Session 4: Conditional Mixture Modeling and Model-based Clustering
Presentation Type
Invited
Student
No
Track
Tools
Abstract
Due to a potentially high number of parameters, finite mixture models are often at the risk of overparameterization even for a moderate number of components. This can lead to overfitting individual components and result in mixture order underestimation. One of the most popular approaches to address this issue is to reduce the number of parameters by considering parsimonious models. The vast majority of techniques in this direction focus on the reparameterization of covariance matrices associated with mixture components. We propose an alternative approach that emphasizes modeling cluster locations. The developed procedure enjoys remarkable modeling flexibility, especially noticeable in the presence of non-compact clusters. Due to an attractive closed form formulation, speedy parameter estimation is available by means of the EM algorithm. The utility of the proposed method is illustrated on synthetic and well-known classification data sets.
Start Date
2-8-2022 9:50 AM
End Date
2-8-2022 10:50 AM
Session 4: Conditional Mixture Modeling and Model-based Clustering
Herold Crest 253 C
Due to a potentially high number of parameters, finite mixture models are often at the risk of overparameterization even for a moderate number of components. This can lead to overfitting individual components and result in mixture order underestimation. One of the most popular approaches to address this issue is to reduce the number of parameters by considering parsimonious models. The vast majority of techniques in this direction focus on the reparameterization of covariance matrices associated with mixture components. We propose an alternative approach that emphasizes modeling cluster locations. The developed procedure enjoys remarkable modeling flexibility, especially noticeable in the presence of non-compact clusters. Due to an attractive closed form formulation, speedy parameter estimation is available by means of the EM algorithm. The utility of the proposed method is illustrated on synthetic and well-known classification data sets.