Add to ORKGIn this paper we present a model based clustering approach which contextually performs dimension reduction and variable selection. In particular we assume that the data have been generated by a linear factor model with latent variables modeled as gaussian mixtures (thus obtaining dimension reduction) and we shrink the factor loadings, resorting to a penalized likelihood method, with an L1 penalty (thus realizing automatic variable selection). We derive an EM algorithm to obtain the penalized model estimates and a modified BIC criterion to select the penalization parameter. We evaluate the performance of the proposed method on simulated and real data
Summary. Variable selection in high-dimensional clustering analysis is an important yet challenging ...
The use of clustering systems is very important in those real-word applications where an efficient, ...
The thesis tackles the problem of uncovering hidden structures in high-dimensional data in the prese...
In this paper we present a model based clustering approach which contextually performs dimension red...
A model-based clustering approach which contextually performs dimension reduction and variable selec...
We propose a method for selecting variables in latent class analysis, which is the most common model...
We consider the problem of variable or feature selection for model-based clustering. The problem of ...
International audienceSeveral methods for variable selection have been proposed in model-based clust...
We propose a method for selecting variables in latent class analysis, which is the most common model...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Variable selection in cluster analysis is important yet challenging. It can be achieved by regulariz...
Summary: Penalized model-based clustering has been proposed for high-dimensional but small sample-si...
Clustering methods with dimension reduction have been receiving considerable wide interest in statis...
Dimensionally reduced model-based clustering methods are recently receiving a wide interest in stati...
Summary. Variable selection in high-dimensional clustering analysis is an important yet challenging ...
Summary. Variable selection in high-dimensional clustering analysis is an important yet challenging ...
The use of clustering systems is very important in those real-word applications where an efficient, ...
The thesis tackles the problem of uncovering hidden structures in high-dimensional data in the prese...
In this paper we present a model based clustering approach which contextually performs dimension red...
A model-based clustering approach which contextually performs dimension reduction and variable selec...
We propose a method for selecting variables in latent class analysis, which is the most common model...
We consider the problem of variable or feature selection for model-based clustering. The problem of ...
International audienceSeveral methods for variable selection have been proposed in model-based clust...
We propose a method for selecting variables in latent class analysis, which is the most common model...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Variable selection in cluster analysis is important yet challenging. It can be achieved by regulariz...
Summary: Penalized model-based clustering has been proposed for high-dimensional but small sample-si...
Clustering methods with dimension reduction have been receiving considerable wide interest in statis...
Dimensionally reduced model-based clustering methods are recently receiving a wide interest in stati...
Summary. Variable selection in high-dimensional clustering analysis is an important yet challenging ...
Summary. Variable selection in high-dimensional clustering analysis is an important yet challenging ...
The use of clustering systems is very important in those real-word applications where an efficient, ...
The thesis tackles the problem of uncovering hidden structures in high-dimensional data in the prese...