arXiv Machine Learning June 11, 2026 By Jarek Duda

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

Moment kernels: a simple and scalable approach for equivariance to rotations and reflections in deep convolutional networks

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