{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DTNEGTLXOAMNNYYVLAU2SIHCUH","short_pith_number":"pith:DTNEGTLX","schema_version":"1.0","canonical_sha256":"1cda434d777018d6e3155829a920e2a1f6d82ac6ae96dd1bbc2d231ac9982ab1","source":{"kind":"arxiv","id":"1804.02624","version":1},"attestation_state":"computed","paper":{"title":"Dimensionality's Blessing: Clustering Images by Underlying Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian-Huang Lai, Siying Liu, Wen-Yan Lin, Yasuyuki Matsushita","submitted_at":"2018-04-08T03:52:09Z","abstract_excerpt":"Many high dimensional vector distances tend to a constant. This is typically considered a negative \"contrast-loss\" phenomenon that hinders clustering and other machine learning techniques. We reinterpret \"contrast-loss\" as a blessing. Re-deriving \"contrast-loss\" using the law of large numbers, we show it results in a distribution's instances concentrating on a thin \"hyper-shell\". The hollow center means apparently chaotically overlapping distributions are actually intrinsically separable. We use this to develop distribution-clustering, an elegant algorithm for grouping of data points by their "},"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":"1804.02624","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-08T03:52:09Z","cross_cats_sorted":[],"title_canon_sha256":"32b778a81c95f979244fd66e203dbdfb3456c3279080d3687adaffba8e25818d","abstract_canon_sha256":"77e9bbb3b775e5d125afe37ea998a6b299e674dde43627a117673f1fa9d07773"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:58.922739Z","signature_b64":"Ezn9UELI9DGJgEFqAqSe4iV6EoDn7zJg+iMPJ/SotESa4q0SZlR7AROvmeMiHdLRsThRaDj8RA33GaWVn5IaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cda434d777018d6e3155829a920e2a1f6d82ac6ae96dd1bbc2d231ac9982ab1","last_reissued_at":"2026-05-18T00:18:58.922270Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:58.922270Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dimensionality's Blessing: Clustering Images by Underlying Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian-Huang Lai, Siying Liu, Wen-Yan Lin, Yasuyuki Matsushita","submitted_at":"2018-04-08T03:52:09Z","abstract_excerpt":"Many high dimensional vector distances tend to a constant. This is typically considered a negative \"contrast-loss\" phenomenon that hinders clustering and other machine learning techniques. We reinterpret \"contrast-loss\" as a blessing. Re-deriving \"contrast-loss\" using the law of large numbers, we show it results in a distribution's instances concentrating on a thin \"hyper-shell\". The hollow center means apparently chaotically overlapping distributions are actually intrinsically separable. We use this to develop distribution-clustering, an elegant algorithm for grouping of data points by their "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.02624","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":"1804.02624","created_at":"2026-05-18T00:18:58.922354+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.02624v1","created_at":"2026-05-18T00:18:58.922354+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.02624","created_at":"2026-05-18T00:18:58.922354+00:00"},{"alias_kind":"pith_short_12","alias_value":"DTNEGTLXOAMN","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DTNEGTLXOAMNNYYV","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DTNEGTLX","created_at":"2026-05-18T12:32:19.392346+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH","json":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH.json","graph_json":"https://pith.science/api/pith-number/DTNEGTLXOAMNNYYVLAU2SIHCUH/graph.json","events_json":"https://pith.science/api/pith-number/DTNEGTLXOAMNNYYVLAU2SIHCUH/events.json","paper":"https://pith.science/paper/DTNEGTLX"},"agent_actions":{"view_html":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH","download_json":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH.json","view_paper":"https://pith.science/paper/DTNEGTLX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.02624&json=true","fetch_graph":"https://pith.science/api/pith-number/DTNEGTLXOAMNNYYVLAU2SIHCUH/graph.json","fetch_events":"https://pith.science/api/pith-number/DTNEGTLXOAMNNYYVLAU2SIHCUH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH/action/storage_attestation","attest_author":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH/action/author_attestation","sign_citation":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH/action/citation_signature","submit_replication":"https://pith.science/pith/DTNEGTLXOAMNNYYVLAU2SIHCUH/action/replication_record"}},"created_at":"2026-05-18T00:18:58.922354+00:00","updated_at":"2026-05-18T00:18:58.922354+00:00"}