Improved Consistent Weighted Sampling Revisited
Abstract
Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets. Consistent Weighted Sampling (CWS) generalizes the Min-Hash scheme to sketch weighted sets and has drawn increasing interest from the community. Due to its constant-time complexity independent of the values of the weights, Improved CWS (ICWS) is considered as the state-of-the-art CWS algorithm. In this paper, we revisit ICWS and analyze its underlying mechanism to show that there actually exists dependence between the two components of the hash-code produced by ICWS, which violates the condition of independence. To remedy the problem, we propose an Improved ICWS (ICWS) algorithm which not only shares the same theoretical computational complexity as ICWS but also abides by the required conditions of the CWS scheme. The experimental results on a number of synthetic data sets and real-world text data sets demonstrate that our ICWS algorithm can estimate the Jaccard similarity more accurately, and also compete with or outperform the compared methods, including ICWS, in classification and top- retrieval, after relieving the underlying dependence.
Keywords
Cite
@article{arxiv.1706.01172,
title = {Improved Consistent Weighted Sampling Revisited},
author = {Wei Wu and Bin Li and Ling Chen and Chengqi Zhang and Philip S. Yu},
journal= {arXiv preprint arXiv:1706.01172},
year = {2017}
}