{"paper":{"title":"Scalability and Total Recall with Fast CoveringLSH","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.IR"],"primary_cat":"cs.DB","authors_text":"Ninh Pham, Rasmus Pagh","submitted_at":"2016-02-08T16:03:11Z","abstract_excerpt":"Locality-sensitive hashing (LSH) has emerged as the dominant algorithmic technique for similarity search with strong performance guarantees in high-dimensional spaces. A drawback of traditional LSH schemes is that they may have \\emph{false negatives}, i.e., the recall is less than 100\\%. This limits the applicability of LSH in settings requiring precise performance guarantees. Building on the recent theoretical \"CoveringLSH\" construction that eliminates false negatives, we propose a fast and practical covering LSH scheme for Hamming space called \\emph{Fast CoveringLSH (fcLSH)}. Inheriting the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.02620","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}