Add to ORKGA model-based clustering approach which contextually performs dimension reduction and variable selection is presented. Dimension reduction is achieved by assuming that the data have been generated by a linear factor model with latent variables modeled as Gaussian mixtures. Variable selection is performed by shrinking the factor loadings though a penalized likelihood method with an L1 penalty. A maximum likelihood estimation procedure via the EM algorithm is developed and a modified BIC criterion to select the penalization parameter is illustrated. The effectiveness of the proposed model is explored in a Monte Carlo simulation study and in a real example.
The efficacy of family-based approaches to mixture model-based clustering and classification depends...
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 ...
A model-based clustering approach which contextually performs dimension reduction and variable selec...
In this paper we present a model based clustering approach which contextually performs dimension red...
Dimensionally reduced model-based clustering methods are recently receiving a wide interest in stati...
In this work we present an exhaustive simulation study aimed at evaluating the clustering performanc...
Summary: Penalized model-based clustering has been proposed for high-dimensional but small sample-si...
Variable selection in cluster analysis is important yet challenging. It can be achieved by regulariz...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Variable selection and other dimensionality reduction methods are more important than ever before. D...
The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous p...
Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clu...
In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian m...
Clustering methods with dimension reduction have been receiving considerable wide interest in statis...
The efficacy of family-based approaches to mixture model-based clustering and classification depends...
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 ...
A model-based clustering approach which contextually performs dimension reduction and variable selec...
In this paper we present a model based clustering approach which contextually performs dimension red...
Dimensionally reduced model-based clustering methods are recently receiving a wide interest in stati...
In this work we present an exhaustive simulation study aimed at evaluating the clustering performanc...
Summary: Penalized model-based clustering has been proposed for high-dimensional but small sample-si...
Variable selection in cluster analysis is important yet challenging. It can be achieved by regulariz...
Variable selection for cluster analysis is a difficult problem. The difficulty originates not only f...
Variable selection and other dimensionality reduction methods are more important than ever before. D...
The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous p...
Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clu...
In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian m...
Clustering methods with dimension reduction have been receiving considerable wide interest in statis...
The efficacy of family-based approaches to mixture model-based clustering and classification depends...
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 ...