Kernel k-Groups via Hartigan's Method.

ABSTRACT: Energy statistics was proposed by Székely in the 80's inspired by Newton's gravitational potential in classical mechanics and it provides a model-free hypothesis test for equality of distributions. In its original form, energy statistics was formulated in euclidean spaces. More recently, it was generalized to metric spaces of negative type. In this paper, we consider a formulation for the clustering problem using a weighted version of energy statistics in spaces of negative type. We show that this approach leads to a quadratically constrained quadratic program in the associated kernel space, establishing connections with graph partitioning problems and kernel methods in machine learning. To find local solutions of such an optimization problem, we propose kernel k-groups, which is an extens

SUBMITTER: Franca G 

PROVIDER: S-EPMC8715390 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature