Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case

Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian

Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis. Notwithstanding, most of the brain-network clustering methods revolve around state clustering and/or node clustering (a.k.a. community detection or topology inference) within states. This work answers first the need of capturing non-linear nodal dependencies by bringing forth a novel feature-extraction mechanism via kernel autoregressive-moving-average modeling. The extract…

黎曼几何与fMRI的结合：建模相关流形和特征向量子空间的优势

Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces

本文提出了一种可扩展的几何框架，通过Off-log度量和Grassmannian子空间判别方法，改进了fMRI数据的分析，提高了敏感性和预测性能。

Riemannian Multi-Manifold Modeling

This paper advocates a novel framework for segmenting a dataset in a Riemannian manifold $M$ into clusters lying around low-dimensional submanifolds of $M$. Important examples of $M$, for which the proposed clustering algorithm is computationally efficient, are the sphere, the set of positive definite matrices, and the Grassmannian. The clustering problem with these examples of $M$ is already useful for numerous application domains such as action identification in video sequences, dynamic texture clustering, brain fiber segmentation in medical imaging, and clustering of deformed images. The pr…