The KL generalization error in unsupervised learning decomposes exactly into model error, data bias, and variance for e-flat models, with closed-form results for ε-PCA on isotropic Gaussians showing optimal rank at the noise floor and a three-regime phase diagram.
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Dynamic distance matrices from Brownian particles on a sphere preserve the static BBS spectral template, with mass redistribution providing diagnostics for ring formation in the underlying point process.
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Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
The KL generalization error in unsupervised learning decomposes exactly into model error, data bias, and variance for e-flat models, with closed-form results for ε-PCA on isotropic Gaussians showing optimal rank at the noise floor and a three-regime phase diagram.
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Frustrated Dynamics of Distance Matrices
Dynamic distance matrices from Brownian particles on a sphere preserve the static BBS spectral template, with mass redistribution providing diagnostics for ring formation in the underlying point process.