Sparse random projections with sign quantization and a log-ratio estimator concentrate around the Jaccard coefficient of sparse supports, enabling one-bit hashing that outperforms MinHash in document and metagenome experiments.
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Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections
Sparse random projections with sign quantization and a log-ratio estimator concentrate around the Jaccard coefficient of sparse supports, enabling one-bit hashing that outperforms MinHash in document and metagenome experiments.