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Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder · Brendan Cook · Matthew Thorpe · Dejan Slepcev

Thu Jul 16 07:00 AM -- 07:45 AM & Thu Jul 16 06:00 PM -- 06:45 PM (PDT) @ Virtual #None

We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the degeneracy of Laplacian semi-supervised learning in this regime. The method replaces the assignment of label values at training points with the placement of sources and sinks, and solves the resulting Poisson equation on the graph. The outcomes are provably more stable and informative than those of Laplacian learning. Poisson learning is efficient and simple to implement, and we present numerical experiments showing the method is superior to other recent approaches to semi-supervised learning at low label rates on MNIST, FashionMNIST, and Cifar-10. We also propose a graph-cut enhancement of Poisson learning, called Poisson MBO, that gives higher accuracy and can incorporate prior knowledge of relative class sizes.

Author Information

Jeff Calder (University of Minnesota)
Brendan Cook (University of Minnesota)
Matthew Thorpe (University of Manchester)
Dejan Slepcev (Carnegie Mellon University)