Data Science Seminar Series: Florence Forbes

on the December 20, 2018

15:45 to 17:00
The Data Science Seminar Series bring high profile researchers to Université Grenoble Alpes to show the increasing role of data science in modern research. It is open to all researchers of Université Grenoble Alpes. For the 4th seminar of this year, we will welcome Florence Forbes from Mistis - Inria.
Florence Forbes from Mistis - Inria will give a talk on “Inverse regression approach to robust non-linear high-to-low dimensional mapping”. The event will take place on Thursday 20 December at IM2AG building. The event will be followed by a coffee break.


We address the issue of nonlinear regression with outliers, possibly in high dimension, without specifying the form of the link function and under a parametric approach. Nonlinearity is handled via an underlying mixture of affine regressions. Each regression is encoded in a joint multivariate Gaussian or Student distribution on the responses and covariates. This joint modeling allows the use of an inverse regression strategy to handle the high dimensionality of the data, while the heavy tail of the Student distribution limits the contamination by outlying data. The possibility to add a number of latent variables similar to factors to the model further reduces its sensitivity to noise or model misspecification. The mixture model setting has the advantage of providing a natural inference procedure using an EM algorithm. The tractability and flexibility of the algorithm are illustrated in simulations and real high-dimensional data with good performance that compares favorably with other existing methods.

Key-words: non linear regression, mixture of regressions, inverse regression, high dimension, EM algorithm.


R code available at:

Related documents
A. Deleforge, F. Forbes and R. Horaud, High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables. Statistics & Computing, 2015.

A. Deleforge, F. Forbes, S. Ba and R. Horaud, Hyper-spectral image analysis with Partially-Latent Regression and spatial Markov dependencies. IEEE journal of selected topics in signal processing, 2015.

F. Forbes, Inverse regression approach to (robust) non-linear high-to-low dimensional mapping, December 2018, slideshow

E. Perthame, F. Forbes and A. Deleforge, Inverse regression approach to robust non-linear high-to-low dimensional mapping, Journal of Multivariate Analysis, 163, p.1-14, January 2018.

Published on January 28, 2019

Practical informations


Bâtiment IM2AG, bulding F, Amphi 18, campus universitaire de Grenoble