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
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.