Add to ORKGWe present a new latent-variable model em-ploying a Gaussian mixture integrated with a feature selection procedure (the Bernoulli part of the model) which together form a ”Latent Bernoulli-Gauss ” distribution. The model is applied to MAP estimation, cluster-ing, feature selection and collaborative filter-ing and fares favorably with the state-of-the-art latent-variable models.
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is ...
Effective feature representation is the key to success of machine learning applications. Recently, m...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
For clustering objects, we often collect not only continuous variables, but binary attributes as wel...
In Beunckens et al. (2006), we propose a so-called latent-class mixture model, bringing to-gether fe...
Finite mixture models have come to play a very prominent role in modelling data. The finite mixture ...
Abstract: Finite mixture models have come to play a very prominent role in modelling data. The finit...
Gaussian processes (GPs) are ubiquitously used in science and engineering as metamodels. Standard GP...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Latent variable models allow capturing the hidden structure underlying the data. In particular, feat...
Abstract. Density modeling is notoriously difficult for high dimensional data. One approach to the p...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is ...
The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous p...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
Abstract — Variable selection is a crucial part of building regression models, and is preferably don...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is ...
Effective feature representation is the key to success of machine learning applications. Recently, m...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
For clustering objects, we often collect not only continuous variables, but binary attributes as wel...
In Beunckens et al. (2006), we propose a so-called latent-class mixture model, bringing to-gether fe...
Finite mixture models have come to play a very prominent role in modelling data. The finite mixture ...
Abstract: Finite mixture models have come to play a very prominent role in modelling data. The finit...
Gaussian processes (GPs) are ubiquitously used in science and engineering as metamodels. Standard GP...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Latent variable models allow capturing the hidden structure underlying the data. In particular, feat...
Abstract. Density modeling is notoriously difficult for high dimensional data. One approach to the p...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is ...
The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous p...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...
Abstract — Variable selection is a crucial part of building regression models, and is preferably don...
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is ...
Effective feature representation is the key to success of machine learning applications. Recently, m...
Multivariate categorical data occur in many applications of machine learning. One of the main diffic...