Our mini is scheduled on May 17, staring at 9:35am Central time (New Orleans)
Abstract
Nonlinear Laplacian operators on graphs and manifolds appear frequently in computational mathematics as they are widely used in a diverse range of applications, including data and image processing problems such as clustering, semi-supervised learning, segmentation. A multitude of recent work has shown that these operators may increase the performance of classical algorithms based on linear Laplacians. This has led to several developments on both the theoretical and the numerical aspects of nonlinear Laplacian operators on graphs and manifolds, including non-differential extreme cases such as the infinity-Laplacian, the 1-Laplacian and p-Laplacians with negative exponent. In this minisymposium we aim at sampling both theoretical and applied recent work in this active area of research.
Speakers:
Martin Burger — Nonlinear Spectral Decompositions Related to P-Laplacians
Qinglan Xia — P-Laplacians on Graphs with a Negative Exponent, and its Relation to Branched Optimal Transportation
Dong Zhang — Piecewise Multilinear Extension and Spectral Theory for Function Pairs
Piero Deidda — Nodal Domain Count and Spectral Properties of Generalized P-Laplacians on Graphs
Abderrahim Elmoataz — Game P-Laplacian on Graph: from Tug-of-War Games to Unified Processing in Image and Points Clouds Processing
Dimosthenis Pasadakis — Multiway P-Spectral Clustering on Grassmann Manifolds
Pan Li, Strongly Local Hypergraph Diffusions for Clustering and Semi-Supervised Learning