Add to ORKGThe regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid commonly faced numerical difficulties encountered with EM. Its principle is applied to Gaussian and Student-t mixtures, resulting in reliable density estimates, the model complexity being kept low. Besides, the regularized models are robust to various noise types. Finally, it is shown..
This paper introduces a new family of local density separations for assessing robustness of finite-di...
PRIOR AND CANDIDATE MODELS IN THE BAYESIAN ANALYSIS OF FINITE MIXTURES This paper discusses the prob...
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we pro...
The regularized Mahalanobis distance is proposed in the framework of nite mixture models to avoid co...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algor...
Finite mixture models are used in statistics and other disciplines, but inference for mixture models...
This thesis deals with classification based on mixture models, mainly on models finite normal. At fi...
Abstract. Bayesian approaches to density estimation and clustering using mixture distributions allow...
This paper discusses the problem of fitting mixture models to input data. When an input stream is an...
In finite mixture modelling, it is crucial to select the number of components for a data set. We hav...
In finite mixture modelling, it is crucial to select the number of components for a data set. We hav...
peer reviewedWe observe a n-sample, the distribution of which is assumed to belong, or at least to b...
This thesis studies two types of research problems under finite mixture models. The first type is mi...
This paper introduces a new family of local density separations for assessing robustness of finite-di...
PRIOR AND CANDIDATE MODELS IN THE BAYESIAN ANALYSIS OF FINITE MIXTURES This paper discusses the prob...
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we pro...
The regularized Mahalanobis distance is proposed in the framework of nite mixture models to avoid co...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algor...
Finite mixture models are used in statistics and other disciplines, but inference for mixture models...
This thesis deals with classification based on mixture models, mainly on models finite normal. At fi...
Abstract. Bayesian approaches to density estimation and clustering using mixture distributions allow...
This paper discusses the problem of fitting mixture models to input data. When an input stream is an...
In finite mixture modelling, it is crucial to select the number of components for a data set. We hav...
In finite mixture modelling, it is crucial to select the number of components for a data set. We hav...
peer reviewedWe observe a n-sample, the distribution of which is assumed to belong, or at least to b...
This thesis studies two types of research problems under finite mixture models. The first type is mi...
This paper introduces a new family of local density separations for assessing robustness of finite-di...
PRIOR AND CANDIDATE MODELS IN THE BAYESIAN ANALYSIS OF FINITE MIXTURES This paper discusses the prob...
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we pro...