Dartminhash Fast Sketching For Weighted Sets

Christiani Tobias. Arxiv 2020

[Paper]    
ARXIV Independent

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 \(x \in \mathbb{R}{\geq 0}^{d}\) and computes \(k\) independent minhashes in expected time \(O(k log k + \Vert x \Vert{0}log( \Vert x \Vert_1 + 1/\Vert x \Vert_1))\), 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 \(\Vert x \Vert_0\) compared to ICWS (ICDM ‘10) and BagMinhash, obtaining \(10\)x 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 \(O(LK + \Vert x \Vert_0)\), improving on prior work even in the case of unweighted sets.

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