The aim of this review is to provide a comprehensive survey of statistical challenges in
neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging …

We develop a new geometric approach for high resolution Doppler processing based on the
Riemannian geometry of Toeplitz covariance matrices and the notion of Riemannian p …

Statistical analysis for populations of networks is widely applicable, but challenging, as
networks have strongly non-Euclidean behaviour. Graph space is an exhaustive framework …

Stochastic and finite-sum optimization problems are central to machine learning. Numerous
specializations of these problems involve nonlinear constraints where the parameters of …

The aim of the present research work is to investigate algorithms to compute empirical
averages of finite sets of sample-points over the Stiefel manifold by extending the notion of …

The correlation matrix is a typical representation of node interactions in functional brain
network analysis. The analysis of the correlation matrix to characterize brain networks …

A Fréchet mean of a random variable Y with values in a metric space (Q, d) is an element of
the metric space that minimizes q↦ E d (Y, q) 2. This minimizer may be non-unique. We …

In many applications, the data can be represented by covariance matrices or correlation
matrices between several signals (EEG, MEG, fMRI), physical quantities (cells, genes), or …

Statistical analysis for populations of networks is widely applicable, but challenging as
networks have strongly non-Euclidean behavior. Graph Space is an exhaustive framework …

When one computes an average of positive definite (PD) matrices, the preservation of
additional matrix structure is desirable for interpretations in applications. An interesting and …