This R code implements the GSPPCA algorithm for high-dimensional unsupervised feature selection. The relevant functions are provided in the GSPPCA.R file and a little demo is in the demoGSPPCA.R file.
[1] C. Bouveyron, P. Latouche, and P.-A. Mattei, Bayesian Variable Selection for Globally Sparse Probabilistic PCA, Electronic Journal of Statistics, vol. 12 (2), pp. 3036-3070, 2018
[2] P.-A. Mattei, C. Bouveyron, and P. Latouche, Globally Sparse Probabilistic PCA, Proc. AISTATS 2016, pp. 976-984
[3] F. Orlhac, P.-A. Mattei, C. Bouveyron, and N. Ayache, Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity, Journal of Chemometrics, vol. 33 (2), e3097, 2019
IMPORTANT REMARK: we use the model described in [1] rather than [2]. These models simply differ by the parametrization of alpha.
pierre-alexandre.mattei[at]inria.fr