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

• URL: http://arxiv.org/abs/2008.03454v2
• Date: Wed, 26 Aug 2020 03:11:48 GMT
• Title: $k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
• Authors: Daniel Fryer, Hien Nguyen, Pascal Castellazzi
• Abstract summary: We state theoretical properties for $k$-means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space. We then provide a novel application for this method, to time-series clustering of pixels in a sequence of Synthetic Aperture Radar images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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