$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| _version_ | 1866914995549241344 |
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| author | Fryer, Daniel Nguyen, Hien Castellazzi, Pascal |
| author_facet | Fryer, Daniel Nguyen, Hien Castellazzi, Pascal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2008_03454 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | $k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences Fryer, Daniel Nguyen, Hien Castellazzi, Pascal Machine Learning Image and Video Processing 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. |
| title | $k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences |
| topic | Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2008.03454 |