{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:3WO4VRIMQGOCG4YG4NK6YZCRGF","short_pith_number":"pith:3WO4VRIM","schema_version":"1.0","canonical_sha256":"dd9dcac50c819c237306e355ec6451316de9bf82d9ec3e87cd511f76177007f1","source":{"kind":"arxiv","id":"1603.02745","version":1},"attestation_state":"computed","paper":{"title":"Non-parametric latent modeling and network clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Fran\\c{c}ois Bavaud","submitted_at":"2016-03-09T00:05:49Z","abstract_excerpt":"The paper exposes a non-parametric approach to latent and co-latent modeling of bivariate data, based upon alternating minimization of the Kullback-Leibler divergence (EM algorithm) for complete log-linear models. For categorical data, the iterative algorithm generates a soft clustering of both rows and columns of the contingency table. Well-known results are systematically revisited, and some variants are presumably original. In particular, the consideration of square contingency tables induces a clustering algorithm for weighted networks, differing from spectral clustering or modularity maxi"},"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":"1603.02745","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-09T00:05:49Z","cross_cats_sorted":[],"title_canon_sha256":"147aa7381d482e00bb669d05d8b93c75c3b8fc9180c804a3c0403db2f43ab2e2","abstract_canon_sha256":"e6083af764b6401d3dfc162f5e0f72f28b5949fd92a6d9544ceb86b6ce115b04"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:19.926272Z","signature_b64":"mHbEmMMiDjlcQNthZQbf9pW1VFowDUVicYzZAAJbyMJw9kzQ4OJbFQ3weLjKx8EPTg4BmLmUpue5wjJL3x/fBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd9dcac50c819c237306e355ec6451316de9bf82d9ec3e87cd511f76177007f1","last_reissued_at":"2026-05-18T01:19:19.925869Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:19.925869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Non-parametric latent modeling and network clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Fran\\c{c}ois Bavaud","submitted_at":"2016-03-09T00:05:49Z","abstract_excerpt":"The paper exposes a non-parametric approach to latent and co-latent modeling of bivariate data, based upon alternating minimization of the Kullback-Leibler divergence (EM algorithm) for complete log-linear models. For categorical data, the iterative algorithm generates a soft clustering of both rows and columns of the contingency table. Well-known results are systematically revisited, and some variants are presumably original. In particular, the consideration of square contingency tables induces a clustering algorithm for weighted networks, differing from spectral clustering or modularity maxi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.02745","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":"1603.02745","created_at":"2026-05-18T01:19:19.925933+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.02745v1","created_at":"2026-05-18T01:19:19.925933+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.02745","created_at":"2026-05-18T01:19:19.925933+00:00"},{"alias_kind":"pith_short_12","alias_value":"3WO4VRIMQGOC","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_16","alias_value":"3WO4VRIMQGOCG4YG","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_8","alias_value":"3WO4VRIM","created_at":"2026-05-18T12:29:58.707656+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/3WO4VRIMQGOCG4YG4NK6YZCRGF","json":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF.json","graph_json":"https://pith.science/api/pith-number/3WO4VRIMQGOCG4YG4NK6YZCRGF/graph.json","events_json":"https://pith.science/api/pith-number/3WO4VRIMQGOCG4YG4NK6YZCRGF/events.json","paper":"https://pith.science/paper/3WO4VRIM"},"agent_actions":{"view_html":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF","download_json":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF.json","view_paper":"https://pith.science/paper/3WO4VRIM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.02745&json=true","fetch_graph":"https://pith.science/api/pith-number/3WO4VRIMQGOCG4YG4NK6YZCRGF/graph.json","fetch_events":"https://pith.science/api/pith-number/3WO4VRIMQGOCG4YG4NK6YZCRGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF/action/storage_attestation","attest_author":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF/action/author_attestation","sign_citation":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF/action/citation_signature","submit_replication":"https://pith.science/pith/3WO4VRIMQGOCG4YG4NK6YZCRGF/action/replication_record"}},"created_at":"2026-05-18T01:19:19.925933+00:00","updated_at":"2026-05-18T01:19:19.925933+00:00"}