Add to ORKGFlexible and reliable probability density estimation is fundamental in unsupervised learning and classification. Finite Gaussian mixture models are commonly used to serve this purpose. However, they fail to estimate unknown probability density functions when used for nonparametric probability density estimation, as severe numerical difficulties may occur when the number of components increases. In this paper, we propose fully nonparametric density estimation by penalizing the covariance matrices of the mixture components according to the regularized Mahalanobis distance. As a consequence, the singularities in the loglikelihood function are avoided and the quality of the estimation models is significantly improved
We compare two regularization methods which can be used to im-prove the generalization capabilities ...
International audienceIn statistics, it is usually difficult to estimate the probability density fun...
When the dimensionality of the feature space increases and takes beyond a certain point, the classif...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid ...
In this paper we review a nonparametric Bayesian estimation technique in mixture of distributions em...
In this paper, the estimation of conditional densities between continuous random variables from nois...
Finite gaussian mixture models are widely used in statistics thanks to their great flexibility. Howe...
We establish that the Dirichlet location scale mixture of normal priors and the logistic Gaussian pr...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
In this paper, a distance-based method for both multivariate non-parametric density and conditional ...
Bayesian nonparametric methods have recently gained popularity in the context of density estimation....
International audienceA two-class mixture model, where the density of one of the components is known...
This thesis proposes Gaussian Mixtures as a flexible semiparametric tool for density estimation and ...
Abstract Suppose independent observations X i , i = 1, . . . , n are observed from a mixture model f...
We compare two regularization methods which can be used to im-prove the generalization capabilities ...
International audienceIn statistics, it is usually difficult to estimate the probability density fun...
When the dimensionality of the feature space increases and takes beyond a certain point, the classif...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
The regularized Mahalanobis distance is proposed in the framework of finite mixture models to avoid ...
In this paper we review a nonparametric Bayesian estimation technique in mixture of distributions em...
In this paper, the estimation of conditional densities between continuous random variables from nois...
Finite gaussian mixture models are widely used in statistics thanks to their great flexibility. Howe...
We establish that the Dirichlet location scale mixture of normal priors and the logistic Gaussian pr...
Flexible and reliable probability density estimation is fundamental in unsupervised learning and cla...
In this paper, a distance-based method for both multivariate non-parametric density and conditional ...
Bayesian nonparametric methods have recently gained popularity in the context of density estimation....
International audienceA two-class mixture model, where the density of one of the components is known...
This thesis proposes Gaussian Mixtures as a flexible semiparametric tool for density estimation and ...
Abstract Suppose independent observations X i , i = 1, . . . , n are observed from a mixture model f...
We compare two regularization methods which can be used to im-prove the generalization capabilities ...
International audienceIn statistics, it is usually difficult to estimate the probability density fun...
When the dimensionality of the feature space increases and takes beyond a certain point, the classif...