$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences

Abstract

We state theoretical properties for $k$-means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representation of these data. We then provide a novel application for this method, to time-series clustering of pixels in a sequence of Synthetic Aperture Radar images, via their finite-lag autocovariance matrices.

This work has been submitted to the IEEE for possible publication