9 citations · 14 across the 7 of their papers we have counts for
3 papers · 1 filter
Sparse PCA with False Discovery Rate Controlled Variable Selection
Jasin Machkour, Arnaud Breloy, Michael Muma +2
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension. By imposing loading vectors to be sparse, it performs the…
FEMDA: a unified framework for discriminant analysis
Pierre Houdouin, Matthieu Jonckheere, Frederic Pascal
Although linear and quadratic discriminant analysis are widely recognized classical methods, they can encounter significant challenges when dealing with non-Gaussian distributions…
Regularized EM algorithm
Pierre Houdouin, Esa Ollila, Frederic Pascal
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing (local) maximum likelihood estimate (MLE). It can be used in an extensive range of proble…