Classification in the Siegel space for vectorial autoregressive data

DOI10.1007/978-3-030-80209-7_74zbMath1490.53021OpenAlexW3180966603MaRDI QIDQ2117928

Yann Cabanes, Frank Nielsen

Publication date: 22 March 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-80209-7_74

zbMATH Keywords

Riemannian manifoldsectional curvatureinformation geometrymachine learningSiegel spaceRiemannian exponential mapRiemannian logarithm mapvar model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Positive matrices and their generalizations; cones of matrices (15B48) Non-Euclidean differential geometry (53A35) Differential geometric aspects of statistical manifolds and information geometry (53B12)

Cites Work

• On approximating the Riemannian 1-center
• Toeplitz Hermitian positive definite matrix machine learning based on Fisher metric
• The Kähler Mean of Block-Toeplitz Matrices with Toeplitz Structured Blocks