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Publication 14-CNA-029

Consistency of Cheeger and Ratio Graph Cuts

Nicolas García Trillos
Department of Mathematical Sciences
Carnegie Mellon University
Pittsburgh, PA 15213

Dejan Slepčev
Department of Mathematical Sciences
Carnegie Mellon University
Pittsburgh, PA 15213

James Von Brecht
Department of Mathematics and Statistics
California State University
Long Beach, Ca 90840, USA

Thomas Laurent
Department of Mathematics
Loyola Marymount University
Los Angeles, Ca 90045, USA

Xavier Bresson
Institute of Electrical Engineering
Swiss Federal Institute Of Technology (Epfl)
1015 Lausanne, Switzerland

Abstract: This paper establishes the consistency of a family of graph-cut-based algorithms for clustering of data clouds. We consider point clouds obtained as samples of a ground-truth measure. We investigate approaches to clustering based on minimizing objective functionals defined on proximity graphs of the given sample. Our focus is on functionals based on graph cuts like the Cheeger and ratio cuts. We show that minimizers of the these cuts converge as the sample size increases to a minimizer of a corresponding continuum cut (which partitions the ground truth measure). Moreover, we obtain sharp conditions on how the connectivity radius can be scaled with respect to the number of sample points for the consistency to hold. We provide results for two-way and for multiway cuts. Furthermore we provide numerical experiments that illustrate the results and explore the optimality of scaling in dimension two.

Get the paper in its entirety as  14-CNA-029.pdf

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