Gaussian Distributions on Riemannian Symmetric Spaces: Statistical Learning With Structured Covariance Matrices

The Riemannian geometry of covariance matrices has been essential to several successful applications, in computer vision, biomedical signal and image processing, and radar data processing. For these applications, an important ongoing challenge is to develop Riemannian-geometric tools which are adapt...

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Veröffentlicht in:	IEEE transactions on information theory 2018-02, Vol.64 (2), p.752-772
Hauptverfasser:	Said, Salem, Hajri, Hatem, Bombrun, Lionel, Vemuri, Baba C.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Classification Computer Science Computer vision Correlation analysis Covariance matrices Covariance matrix Data processing Density Estimation Extraterrestrial measurements Gaussian distribution Gaussian mixture model Image processing Machine learning Mathematical analysis Matrix methods Normal distribution Probabilistic models Radar data Radar imaging Riemannian barycentre Riemannian symmetric space Signal and Image Processing Signal processing Simulation Statistical analysis Statistical inference Statistical learning Structured covariance matrix Symmetric matrices
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