Riemannian Statistics Meets Random Matrix Theory: Toward Learning From High-Dimensional Covariance Matrices

DOI10.1109/tit.2022.3199479arXiv2203.00204WikidataQ115263137 ScholiaQ115263137MaRDI QIDQ6196113

Cyrus Mostajeran, Simon Heuveline, Salem Said

Publication date: 14 March 2024

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2203.00204

zbMATH Keywords

covariance matrixrandom matrix theoryhigh-dimensional dataRiemannian Gaussian distributionblock-Toeplitz covariance matrix

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Measures of association (correlation, canonical correlation, etc.) (62H20) Random matrices (probabilistic aspects) (60B20) Random matrices (algebraic aspects) (15B52) Toeplitz, Cauchy, and related matrices (15B05)

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