We present a generalized version of spectral clustering using the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian. We show that the second eigenvecto...
In the machine learning community it is generally believed that graph Laplacians corresponding to a finite sample of data points converge to a continuous Laplace operator if the s...
Matthias Hein, Jean-Yves Audibert, Ulrike von Luxb...
Ranking nodes in graphs is of much recent interest. Edges, via the graph Laplacian, are used to encourage local smoothness of node scores in SVM-like formulations with generalizat...
This paper exploits the properties of the commute time for the purposes of graph matching. Our starting point is the random walk on the graph, which is determined by the heat-kern...
We introduce a graph Laplacian based algorithm for the tomography reconstruction of a planar object from its projections taken at random unknown directions. The algorithm is shown ...
Ronald R. Coifman, Yoel Shkolnisky, Fred J. Sigwor...