Frederic Pascal
16 papers 198 citations h-index 7 2012–2024
Sparse PCA with False Discovery Rate Controlled Variable Selection
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 double duty of...
FEMDA: a unified framework for discriminant analysis
Although linear and quadratic discriminant analysis are widely recognized classical methods, they can encounter significant challenges when dealing with non-Gaussian distributions or contaminated...
A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data
This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian mixture models,...
Choosing the parameter of the Fermat distance: navigating geometry and noise
The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve the results given...
Random matrix theory improved Fr\'echet mean of symmetric positive definite matrices
In this study, we consider the realm of covariance matrices in machine learning, particularly focusing on computing Fr\'echet means on the manifold of symmetric positive definite matrices,...
Robust classification with flexible discriminant analysis in heterogeneous data
Linear and Quadratic Discriminant Analysis are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying...
PCA-based Multi Task Learning: a Random Matrix Approach
The article proposes and theoretically analyses a \emph{computationally efficient} multi-task learning (MTL) extension of popular principal component analysis (PCA)-based supervised learning...
FEMDA: Une m\'ethode de classification robuste et flexible
Linear and Quadratic Discriminant Analysis (LDA and QDA) are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the...
Regularized EM algorithm
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 problems, including...
Riemannian classification of EEG signals with missing values
This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices. It relies on the observed-data likelihood within an...
A flexible EM-like clustering algorithm for noisy data
Though very popular, it is well known that the EM for GMM algorithm suffers from non-Gaussian distribution shapes, outliers and high-dimensionality. In this paper, we design a new robust...
Optimal Design of the Adaptive Normalized Matched Filter Detector
This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance...
Convergence and Fluctuations of Regularized Tyler Estimators
This article studies the behavior of regularized Tyler estimators (RTEs) of scatter matrices. The key advantages of these estimators are twofold. First, they guarantee by construction a good...
Robust Estimates of Covariance Matrices in the Large Dimensional Regime
This article studies the limiting behavior of a class of robust population covariance matrix estimators, originally due to Maronna in 1976, in the regime where both the number of available samples...
Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning
This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to compute the maximum...
Algorithme EM r\'egularis\'e
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM). When the sample size is...