Dobriban, Edgar 2015. Efficient computation of limit spectra of sample covariance matrices. Random Matrices: Theory and Applications, Vol. 04, Issue. 04, p. 1550019.

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This limitation primarily arises from the extensive number of steps required in the reverse process and the fixed or variably learned covariance settings, which don’t adequately optimize output ...

Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python ...

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We extracted structural covariance networks from the log Jacobian determinants of 435 in utero T2 weighted image magnetic resonance imaging scans, (n=67 controls, 368 with CHD) acquired during the ...

Abstract: Covariance matrices have found applications in many diverse areas. These include beamforming in array processing; portfolio analysis in finance; classification of data and the handling of ...

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