Dimensionality reduction by clustering of variables while setting aside atypical variables

Clustering of variables is one possible approach for reducing the dimensionality of a dataset. However, all the variables are usually assigned to one of the clusters, even the scattered variables associated with atypical or noise information. The presence of this type of information could obscure the interpretation of the latent variables associated with the clusters, or even give rise to artificial clusters. We propose two strategies to address this problem. The first is a "K +1" strategy, which consists of introducing an additional group of variables,  called the "noise cluster" for simplicity. The second is based on the definition of sparse latent variables. Both strategies result in refined clusters for the identification of more relevant latent variables

Clustering of variables for enhanced interpretability of predictive models

A new strategy is proposed for building easy to interpret predictive models in the context of a high-dimensional dataset, with a large number of highly correlated explanatory variables. The strategy is based on a first step of variables clustering using the CLustering of Variables around Latent Variables (CLV) method. The exploration of the hierarchical clustering dendrogram is undertaken in order to sequentially select the explanatory variables in a group-wise fashion. For model setting implementation, the dendrogram is used as the base-learner in an L2-boosting procedure. The proposed approach, named lmCLV, is illustrated on the basis of a toy-simulated example when the clusters and predictive equation are already known, and on a real case study dealing with the authentication of orange juices based on 1H-NMR spectroscopic analysis. In both illustrative examples, this procedure was shown to have similar predictive efficiency to other methods, with additional interpretability capacity. It is available in the R package ClustVarLV.Comment: 24 pages, 7 figure

La baisse du contentieux est-elle le signe d'une pacification de la relation de travail ?

International audienc

Application of procrustean methods to mid- and near-infrared spectral data

International audienc

Functional Approach for the analysis of time intensity curves using B-splines

International audienceThis article deals with a functional approach based on the projection upon a beta-spline basis in order to analyze Time Intensity curves. The modelization is followed, on the one hand, by the assessment of the repeatability and the discrimination ability of the panelists, and on the other hand, by the determination of a good compromise over repetitions. Finally, a multidimensional analysis enables the comparison of the shapes of the curves associated with the assessors (assessors' signature) and the characterization of the products. The properties of this functional approach are illustrated with TI curves describing sweetness variations of drinks

Application of latent root regression for calibration in near-infrared spectroscopy. Comparison with principal component regression and partial least squares

International audienc

Comparaison de la régression en composantes principales et de la "latent root regression". Application à la prédiction de la production spécifique de granules par spectroscopie proche infrarouge

National audienc

Principal component regression, ridge regression and ridge principal component regression in spectroscopy calibration

International audienc