R software package to implement high-dimensional error-in-variables regression. This package implements CoCoLasso algorithm in settings with additive error or missing data in the covariates. This package also implements a variation of the CoCoLasso algorithm called Block-Descent CoCoLasso (or BD-CoCoLasso), which focuses on a setting where only a small percentage of the features are corrupted (with additive error or missing data)
This package is based on the CoCoLasso algorithm. CoCoLASSO requires a computationally demanding positive semi-definite projection of the covariance matrix for a high dimensional feature set. In a very high-dimensional context where there are both corrupted and uncorrupted covariates and where the portion of corrupted features is relatively small, we take advantage of the block descent minimization to develop a more efficient algorithm called BDCoCoLasso. In an alternating block minimization algorithm, the CoCoLasso corrections are used when updating corrupted coefficient vectors, and a simple LASSO is used for the uncorrupted coefficient vectors. Both sub-problems are convex and hence a global solution can be obtained, even though adaption of the cross-validation step requires care in this setting where there are products of corrupted and uncorrupted matrices.
install.packages("BDcocolasso")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("celiaescribe/BDcocolasso")
See the online vignette for details about the BDcoco model and example usage of the functions.
There exist two settings in which the BD-CoCoLasso can be used : in the simple CoCoLasso version, and in the Block-Descent-CoCoLasso version. The inputs vary according to the chosen algorithm setting, and according to the chosen noise setting.
email : celia.escribe@polytechnique.edu; tianyuan.lu@mail.mcgill.ca; karim.oualkacha@uqam.ca; celia.greenwood@mcgill.ca
We were inspired by the following studies :
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