Variable selection for mixed data clustering: a model-based approach

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Date(s) - 24/04/2017
15 h 30 min - 16 h 30 min

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In this talk, we consider two approaches for selecting variables in latent class analysis. The first approach consists in optimizing the BIC with a modified version of the EM algorithm. This approach simultaneously performs both model selection and parameter inference.
The second approach consists in maximizing the MICL, which considers the clustering task, with an algorithm of alternate optimization. This approach performs model selection without requiring the maximum likelihood estimates for model comparison, then parameter inference is done for the unique selected model. Thus, both approaches avoid the computation of the maximum likelihood estimates for each model comparison. Moreover, they also avoid the use of the standard algorithms for variable selection which are often suboptimal (e.g. stepwise method) and computationally expensive. The case of data with missing values is also discussed. The interest of both proposed criteria is shown on a medical data sets describing 1300
patients by 160000 variables.

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