Section: New Software and Platforms

xLLiM

High dimensional locally linear mapping

Keywords: Clustering - Regression

Scientific Description: Building a regression model for the purpose of prediction is widely used in all disciplines. A large number of applications consists of learning the association between responses and predictors and focusing on predicting responses for the newly observed samples. In this work, we go beyond simple linear models and focus on predicting low-dimensional responses using high-dimensional covariates when the associations between responses and covariates are non-linear.

Functional Description: This is an R package available on the CRAN at https://cran.r-project.org/web/packages/xLLiM/index.html

XLLiM provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (Deleforge et al (2015) ) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) ) based on a mixture of Generalized Student distributions.

News Of The Year: A new Hierarchical version of GLLiM has been developed in collaboration with University of Michigan, USA.

• Participants: Antoine Deleforge, Emeline Perthame and Florence Forbes

• Partner: University of Michigan, Ann Arbor, USA

• Contact: Florence Forbes

• Publications: Inverse regression approach to robust nonlinear high-to-low dimensional mapping - High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables

• URL: https://cran.r-project.org/web/packages/xLLiM/index.html