Papers › DartMinHash: Fast Sketching for Weighted Sets
DartMinHash: Fast Sketching for Weighted Sets
Tobias Christiani
Weighted minwise hashing is a standard dimensionality reduction technique with applications to similarity search and large-scale kernel machines. We introduce a simple algorithm that takes a weighted set x ∈ℝ_(≥0)ᵈ and computes k independent minhashes in expected time O(k logk + ‖x ‖₀log( ‖x ‖₁ + 1/‖x ‖₁)), improving upon the state-of-the-art BagMinHash algorithm (KDD '18) and representing the fastest weighted minhash algorithm for sparse data. Our experiments show running times that scale better with k and ‖x ‖₀ compared to ICWS (ICDM '10) and BagMinhash, obtaining 10x speedups in common use cases. Our approach also gives rise to a technique for computing fully independent locality-sensitive hash values for (L, K)-parameterized approximate near neighbor search under weighted Jaccard similarity in optimal expected time O(LK + ‖x ‖₀), improving on prior work even in the case of unweighted sets.
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