{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:SB7NBKC6LDKE2Q56LGJQRI5TUJ","short_pith_number":"pith:SB7NBKC6","schema_version":"1.0","canonical_sha256":"907ed0a85e58d44d43be599308a3b3a276249e73761d0b3bb189d32d71cd1b22","source":{"kind":"arxiv","id":"1707.00514","version":1},"attestation_state":"computed","paper":{"title":"People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ML","authors_text":"Alexander Cloninger, Brita Roy, Carley Riley, Harlan M. Krumholz","submitted_at":"2017-07-03T12:57:03Z","abstract_excerpt":"We address the problem of defining a network graph on a large collection of classes. Each class is comprised of a collection of data points, sampled in a non i.i.d. way, from some unknown underlying distribution. The application we consider in this paper is a large scale high dimensional survey of people living in the US, and the question of how similar or different are the various counties in which these people live. We use a co-clustering diffusion metric to learn the underlying distribution of people, and build an approximate earth mover's distance algorithm using this data adaptive transpo"},"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":"1707.00514","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-03T12:57:03Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"a0bf8d810be10890fe38febd046ba565f03d786dbc7eb3e42f64127dae40b2ec","abstract_canon_sha256":"57a75be38f4a0bb2d03cedda02797faebfb2bcb40df6938aad6d7523f5697d4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:03.145213Z","signature_b64":"96MXZtT5WUAKvD55XbpWKGHMvhhBJcmmKYY0z+iMkyYWtUuKPPhJhj1lfH2mBV42KMhnNURTEvcWbayYW2CKBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"907ed0a85e58d44d43be599308a3b3a276249e73761d0b3bb189d32d71cd1b22","last_reissued_at":"2026-05-18T00:41:03.144694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:03.144694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"People Mover's Distance: Class level geometry using fast pairwise data adaptive transportation costs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ML","authors_text":"Alexander Cloninger, Brita Roy, Carley Riley, Harlan M. Krumholz","submitted_at":"2017-07-03T12:57:03Z","abstract_excerpt":"We address the problem of defining a network graph on a large collection of classes. Each class is comprised of a collection of data points, sampled in a non i.i.d. way, from some unknown underlying distribution. The application we consider in this paper is a large scale high dimensional survey of people living in the US, and the question of how similar or different are the various counties in which these people live. We use a co-clustering diffusion metric to learn the underlying distribution of people, and build an approximate earth mover's distance algorithm using this data adaptive transpo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.00514","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":"1707.00514","created_at":"2026-05-18T00:41:03.144778+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.00514v1","created_at":"2026-05-18T00:41:03.144778+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.00514","created_at":"2026-05-18T00:41:03.144778+00:00"},{"alias_kind":"pith_short_12","alias_value":"SB7NBKC6LDKE","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"SB7NBKC6LDKE2Q56","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"SB7NBKC6","created_at":"2026-05-18T12:31:43.269735+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/SB7NBKC6LDKE2Q56LGJQRI5TUJ","json":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ.json","graph_json":"https://pith.science/api/pith-number/SB7NBKC6LDKE2Q56LGJQRI5TUJ/graph.json","events_json":"https://pith.science/api/pith-number/SB7NBKC6LDKE2Q56LGJQRI5TUJ/events.json","paper":"https://pith.science/paper/SB7NBKC6"},"agent_actions":{"view_html":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ","download_json":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ.json","view_paper":"https://pith.science/paper/SB7NBKC6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.00514&json=true","fetch_graph":"https://pith.science/api/pith-number/SB7NBKC6LDKE2Q56LGJQRI5TUJ/graph.json","fetch_events":"https://pith.science/api/pith-number/SB7NBKC6LDKE2Q56LGJQRI5TUJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ/action/storage_attestation","attest_author":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ/action/author_attestation","sign_citation":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ/action/citation_signature","submit_replication":"https://pith.science/pith/SB7NBKC6LDKE2Q56LGJQRI5TUJ/action/replication_record"}},"created_at":"2026-05-18T00:41:03.144778+00:00","updated_at":"2026-05-18T00:41:03.144778+00:00"}