High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach

1 code implementation • 6 May 2021 • Boxin Zhao, Percy S. Zhai, Y. Samuel Wang, Mladen Kolar

We propose a neighborhood selection approach to estimate the structure of Gaussian functional graphical models, where we first estimate the neighborhood of each node via a function-on-function regression and subsequently recover the entire graph structure by combining the estimated neighborhoods.

Dimensionality Reduction EEG +1