Loci selection in model-based clustering

Comparison between the IVNS algorithm and three state-of-the-art algorithms from the literature (i.e., GRASP, TS, GRASP+TS [25]) on the first two sets (RanReal and DB) of CCP

The literature on this issue, corresponding to step (a), covers three main ways to perform the selection: the first one is to consider all possible subsets, leading to an

Although we did not find (exactly) this result in the statistical literature on vari- able selection, it can be checked that (10) is a necessary and sufficient condition for

Vincent Vandewalle, Thierry Mottet, Matthieu Marbac. Model-based variable clustering. CMStatis- tics/ERCIM 2017 - 10th International Conference of the ERCIM WG on Computational

Globally the data clustering slightly deteriorates as p grows. The biggest deterioration is for PS-Lasso procedure. The ARI for each model in PS-Lasso and Lasso-MLE procedures

The criterion SICL has been conceived in the model-based clustering con- text to choose a sensible number of classes possibly well related to an exter- nal qualitative variable or a

Indeed, for AIC-3 and other asymptotic criteria, the asymptotic regime needs to be clearly defined which is tricky for the latent block model: a data table is a single element of

We found that the two variable selection methods had comparable classification accuracy, but that the model selection approach had substantially better accuracy in selecting