most citedGrouped variable importance with random forests and application to multiple functional data analysis

130 citations · 252 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CG20161 cited

Data driven estimation of Laplace-Beltrami operator

Frédéric Chazal, Ilaria Giulini, Bertrand Michel

Approximations of Laplace-Beltrami operators on manifolds through graph Lapla-cians have become popular tools in data analysis and machine learning. These discretized operators usu…

math.ST2014107 cited

Robust Topological Inference: Distance To a Measure and Kernel Distance

Frédéric Chazal, Brittany T. Fasy, Fabrizio Lecci +3

Let P be a distribution with support S. The salient features of S can be quantified with persistent homology, which summarizes topological features of the sublevel sets of the dist…

stat.ME2014130 cited

Grouped variable importance with random forests and application to multiple functional data analysis

Baptiste Gregorutti, Bertrand Michel, Philippe Saint-Pierre

The selection of grouped variables using the random forest algorithm is considered. First a new importance measure adapted for groups of variables is proposed. Theoretical insights…

math.AT2014

Subsampling Methods for Persistent Homology

Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci +3

Persistent homology is a multiscale method for analyzing the shape of sets and functions from point cloud data arising from an unknown distribution supported on those sets. When th…

math.ST201414 cited

Improved rates for Wasserstein deconvolution with ordinary smooth error in dimension one

Jérôme Dedecker, Aurélie Fischer, Bertrand Michel

This paper deals with the estimation of a probability measure on the real line from data observed with an additive noise. We are interested in rates of convergence for the Wasserst…