Author

Pietro Lió

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Preprint Aug 2026

Orthogonal Polynomial Approximation for Matrix Log Normalization in Global Covariance Pooling

The decomposition-free logarithm is both faster and more accurate than the spectral logarithm and than the square-root approximations it replaces, and at matched basis and degree the log target beats the square-root target, confirming that the gain comes from the faithful Riemannian map rather than from a better polyno...

Md Rifat Ur Rahman, Md Raihan Khan, Md Sakib Hossain Shovon et al. · 0 citations

Preprint Aug 2026

Orthogonal Polynomial Approximation for Matrix Log Normalization in Global Covariance Pooling

Global Covariance Pooling (GCP) improves deep networks by capturing second-order feature statistics, and is especially effective for fine-grained recognition. Because covariance matrices live on the Symmetric Positive Definite (SPD) manifold, a normalization step is required before the Euclidean classifier. The faithfu...

Md Rifat Ur Rahman, Md Raihan Khan, Md Sakib Hossain Shovon et al. · 0 citations