Document Type
Article - pre-print
Publication Date
2012
Abstract
Unsupervised clustering of scattered, noisy and high-dimensional data points is an important and difficult problem. Tight continuous relaxations of balanced cut problems have recently been shown to provide excellent clustering results. In this paper, we present an explicit-implicit gradient flow scheme for the relaxed ratio cut problem, and prove that the algorithm converges to a critical point of the energy. We also show the efficiency of the proposed algorithm on the two moons dataset.
Original Publication Citation
X. Bresson, T. Laurent, D. Uminsky, and J. von Brecht. Convergence of a Steepest Descent Algorithm for Ratio Cut Clustering, 2012. Unpublished.
Digital Commons @ LMU & LLS Citation
Bresson, Xavier; Laurent, Thomas; Uminsky, David; and von Brecht, James H., "Convergence of a Steepest Descent Algorithm for Ratio Cut Clustering" (2012). Mathematics, Statistics and Data Science Faculty Works. 88.
https://digitalcommons.lmu.edu/math_fac/88
