arXiv Machine Learning By Jarek Duda

Higher order PCA-like rotation-invariant features for detailed shape descriptors modulo rotation

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arXiv:2601. 03326v2 Announce Type: replace-cross Abstract: PCA can be used for rotation invariant features, describing a shape with its $p_{ab}=E[(x_i-E[x_a])(x_b-E[x_b])]$ covariance matrix approximating shape by ellipsoid, allowing for rotation invariants like its traces of powers.

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