Filter and Graph Learning with Optimal Transport

This lecture presents FIGLearn, a method for learning filters and graphs using optimal transport. It covers the signal generation model, the approach to minimize Wasserstein distance, the algorithm involving filter and graph learning steps, limitations in non-convex optimization, an application in missing data inference, and a summary highlighting the flexibility and performance of the approach.