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The manifold hypothesis is a foundational prior in machine learning and high-dimensional statistics, holding that the effective degrees of freedom of high-dimensional observed data are governed by a low-dimensional manifold, with data points lying approximately on a manifold of dimension d much smaller than D.
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10/12/2026
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