{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PFMYJOC47ZKY5BJTNBWW6NM7CK","short_pith_number":"pith:PFMYJOC4","schema_version":"1.0","canonical_sha256":"795984b85cfe558e8533686d6f359f12bfdffa7ece753235720ecb5d90def90d","source":{"kind":"arxiv","id":"1907.03813","version":1},"attestation_state":"computed","paper":{"title":"Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Alessandro Rinaldo, Leman Akoglu, Xiaoyi Gu","submitted_at":"2019-07-08T18:58:35Z","abstract_excerpt":"Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and unsupervised learning problems. In this paper we are concerned with investigating the performance of NN-based methods for anomaly detection. We first show through extensive simulations that NN methods compare favorably to some of the other state-of-the-art algorithms for anomaly detection based on a set of benchmark synthetic datasets. We further consider the performance of NN methods on real datasets, and relate it to the dimensionality of the problem. Next, we analyze the theoretical properties of NN-met"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1907.03813","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2019-07-08T18:58:35Z","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"title_canon_sha256":"6a1d5028ca6bf4c3625d96aa1e6ea9a7abc4cc54a5db260bd62751d6ce2301d6","abstract_canon_sha256":"44cb4d00da68f9cdd7bcf872bb15d6e97be274f69d36cc510b702a2296e8ea36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:06.796989Z","signature_b64":"fYZ74IpruXYycTWFUsTvfkyBu1+m/p4f50zMY9UguiyPuLm88dciug5gXDA1ovE6w03rBvYeKEiwtVkLZYWOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"795984b85cfe558e8533686d6f359f12bfdffa7ece753235720ecb5d90def90d","last_reissued_at":"2026-05-17T23:41:06.796053Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:06.796053Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Alessandro Rinaldo, Leman Akoglu, Xiaoyi Gu","submitted_at":"2019-07-08T18:58:35Z","abstract_excerpt":"Nearest-neighbor (NN) procedures are well studied and widely used in both supervised and unsupervised learning problems. In this paper we are concerned with investigating the performance of NN-based methods for anomaly detection. We first show through extensive simulations that NN methods compare favorably to some of the other state-of-the-art algorithms for anomaly detection based on a set of benchmark synthetic datasets. We further consider the performance of NN methods on real datasets, and relate it to the dimensionality of the problem. Next, we analyze the theoretical properties of NN-met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03813","kind":"arxiv","version":1},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1907.03813","created_at":"2026-05-17T23:41:06.796224+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03813v1","created_at":"2026-05-17T23:41:06.796224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03813","created_at":"2026-05-17T23:41:06.796224+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFMYJOC47ZKY","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFMYJOC47ZKY5BJT","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFMYJOC4","created_at":"2026-05-18T12:33:24.271573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.22688","citing_title":"COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC","ref_index":138,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK","json":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK.json","graph_json":"https://pith.science/api/pith-number/PFMYJOC47ZKY5BJTNBWW6NM7CK/graph.json","events_json":"https://pith.science/api/pith-number/PFMYJOC47ZKY5BJTNBWW6NM7CK/events.json","paper":"https://pith.science/paper/PFMYJOC4"},"agent_actions":{"view_html":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK","download_json":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK.json","view_paper":"https://pith.science/paper/PFMYJOC4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03813&json=true","fetch_graph":"https://pith.science/api/pith-number/PFMYJOC47ZKY5BJTNBWW6NM7CK/graph.json","fetch_events":"https://pith.science/api/pith-number/PFMYJOC47ZKY5BJTNBWW6NM7CK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK/action/storage_attestation","attest_author":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK/action/author_attestation","sign_citation":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK/action/citation_signature","submit_replication":"https://pith.science/pith/PFMYJOC47ZKY5BJTNBWW6NM7CK/action/replication_record"}},"created_at":"2026-05-17T23:41:06.796224+00:00","updated_at":"2026-05-17T23:41:06.796224+00:00"}