2013/11/01 by Huitong Qiu, Qiu, Huitong, Fang Han +5 · 1 citation
Computer Science · Neuroscience · #Blind Source Separation Techniques #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (stat.ML) #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.1311.0219
openalex publication_date 2013/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T,n) and the dimension d can increase, we provide the explicit rate of convergence in parameter estimation. It characterizes the strength one can borrow across different individuals and impact of data dependence on parameter estimation. Empirically, experiments on both synthetic and real resting state functional magnetic resonance imaging (rs-fMRI) data illustrate the effectiveness of the proposed method.