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$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
Published 8 Aug 2020 in stat.ML, cs.LG, and eess.IV | (2008.03454v2)
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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