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Pascal Castellazzi

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

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Aug 26, 2020
Daniel Fryer, Hien Nguyen, Pascal Castellazzi
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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. Copyright may be transferred without notice, after which this version may no longer be accessible 
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