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PRIM-cipal components analysis

stat.ML · 2026-04-16 · unverdicted · novelty 7.0

For elliptical distributions, peeling the k smallest principal components maximizes total variance and Frobenius norm while peeling the k largest minimizes them, proving an unsupervised No Free Lunch theorem for bump-hunting.

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  • PRIM-cipal components analysis stat.ML · 2026-04-16 · unverdicted · none · ref 8

    For elliptical distributions, peeling the k smallest principal components maximizes total variance and Frobenius norm while peeling the k largest minimizes them, proving an unsupervised No Free Lunch theorem for bump-hunting.