A new clustering distance, the largest angle path distance, separates intersecting manifolds using dihedral angles between simplices, with high-probability guarantees and near-linear runtime.
Moderate deviation theorem for the Neyman-Pearson statistic in testing uniformity
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abstract
We show that for local alternatives to uniformity which are determined by a sequence of square integrable densities the moderate deviation (MD) theorem for the corresponding Neyman-Pearson statistic does not hold in the full range for all unbounded densities. We give a sufficient condition under which MD theorem holds. The proof is based on Mogulskii's inequality.
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Robust Multi-Manifold Clustering via Simplex Paths
A new clustering distance, the largest angle path distance, separates intersecting manifolds using dihedral angles between simplices, with high-probability guarantees and near-linear runtime.