arXiv:2008.03454
공식 코드 · Papers with Code
$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
원문 초록
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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A Review of the Long Horizon Forecasting Problem in Time Series Analysis
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A Better Alternative to Piecewise Linear Time Series Segmentation
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?
Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems
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