Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models

Regularized Parameter Estimation in High-Dimensional Gaussian Mixture Models

Finite gaussian mixture models are widely used in statistics thanks to their great flexibility. However, parameter estimation for gaussian mixture models with high dimensionality can be challenging because of the large number of parameters that need to be estimated. In this letter, we propose a penalized likelihood estimator to address this difficulty. The [Formula: see text]-type penalty we im...

High-Dimensional Clustering with Sparse Gaussian Mixture Models

We consider the problem of clustering high-dimensional data using Gaussian Mixture Models (GMMs) with unknown covariances. In this context, the ExpectationMaximization algorithm (EM), which is typically used to learn GMMs, fails to cluster the data accurately due to the large number of free parameters in the covariance matrices. We address this weakness by assuming that the mixture model consis...

Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation

 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...

Two-way Gaussian mixture models for high dimensional classification

Mixture discriminant analysis (MDA) has gained applications in a wide range of engineering and scientific fields. In this paper, under the paradigm of MDA, we propose a two-way Gaussian mixture model for classifying high dimensional data. This model regularizes the mixture component means by dividing variables into groups and then constraining the parameters for the variables in the same group ...

Penalized Likelihood Estimation in Gaussian Mixture Models