Fig. 1.

Graph Laplacian learning with Exponential Family Noise (GLEN). A. Diagram showing how GLEN jointly optimizes the unknown graph $G$ and the unknown latent smooth representation $\mathbf{Y}$ given observations $\mathbf{X}$. GLEN alternates these two steps: (i) a graph learning step that infers $G$ from current $\mathbf{Y}$ estimation and (ii) a denoising step that smooths binary observations $\mathbf{X}$ using the current $G$ to obtain new $\mathbf{Y}$. Each of the $M$ columns of $\mathbf{Y}$ (the red vertical waveform) is a smooth graph signal over $N$ nodes. B. Example of a Bernoulli graph signal $\mathbf{x}$. A solid blue bar indicates a 1 and an empty bar indicates a 0. C. Example of a smooth graph signal $\mathbf{y}$ underlying the noisy Poisson observations. D. Example of temporally correlated smooth graph signals.