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Data-dependent hashing via nonlinear spectral gaps.

STOC '18: Symposium on Theory of Computing Los Angeles CA USA June, 20..., pp.787-800, (2018)

Cited by: 19|Views76
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Abstract

We establish a generic reduction from _nonlinear spectral gaps_ of metric spaces to data-dependent Locality-Sensitive Hashing, yielding a new approach to the high-dimensional Approximate Near Neighbor Search problem (ANN) under various distance functions. Using this reduction, we obtain the following results: * For _general_ d-dimension...More

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