… threads of the components of the proposed framework are: a novel kernel-based … the network
time-series, and the Riemannian geometry of the Grassmann manifold (Grassmannian) into …

… in this study. Having obtained the features and to identify clusters, this study builds on
Riemannian multi-manifold … Features were extracted by a kernel-based ARMA model, with column …

… Grassmann manifold. The second one utilizes (non-linear) dependencies among network
nodes by introducing kernel-based … within the brain network is dynamic, and two or more brain …

… Grassmann manifold. Firstly, a low rank representation method based on matrix recovery is
introduced into the Grassmann manifold … Then a deep neural network is proposed to reduce …

… aims to develop a new manifold-based modelling approach for the causal analysis of …
Kernel-based analysis of functional brain connectivity on grassmann manifold. In N. Navab, …

… into the Grassmann manifold. The second one utilizes (non-linear) dependencies … network
nodes by introducing kernel-based partial correlations to generate points in the manifold of …

… of network dynamics on Riemannian manifolds… manifold of positive (semi-)definite symmetric
matrices, while low-rank linear subspaces can be considered as points of the Grassmannian…

… In this section, we compare the performance of various kernel-based approaches. Besides
FLGP, we consider GPs with radial basis function kernels (RBF), kernel support vector …

… Each group is assigned an adjacency matrix that represents the underpinning groupwise
functional brain connectivity graph. The nodes represent the observed channels, and those …

… “Kernelbased classification for brain connectivity graphs on the Riemannian manifold of
positive definite matrices”. … on Grassmann Manifold“. In MICCAI 2015. Lecture Notes in CS, vol. …