{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:L5VC5UAXAAEEL4R4DCY76ASHZV","short_pith_number":"pith:L5VC5UAX","schema_version":"1.0","canonical_sha256":"5f6a2ed017000845f23c18b1ff0247cd539248d555098125d3a3a21524e6141c","source":{"kind":"arxiv","id":"2303.11848","version":1},"attestation_state":"computed","paper":{"title":"Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Antonios Gasteratos, George Pavlidis, Spyridon Mouroutsos, Vasileios Sevetlidis","submitted_at":"2023-03-21T13:48:53Z","abstract_excerpt":"This study proposes a novel approach for solving the PU learning problem based on an anomaly-detection strategy. Latent encodings extracted from positive-labeled data are linearly combined to acquire new samples. These new samples are used as embeddings to increase the density of positive-labeled data and, thus, define a boundary that approximates the positive class. The further a sample is from the boundary the more it is considered as a negative sample. Once a set of negative samples is obtained, the PU learning problem reduces to binary classification. The approach, named Dens-PU due to its"},"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":"2303.11848","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-03-21T13:48:53Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"c2a0186a7c6724b7543147828c5c4ba7887b74f2f4b97002d370f83d7481744c","abstract_canon_sha256":"579e314d2747278ba22e9524f8c13d11df6361b0bd5a635dc93402cdac8ac2fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:32.694351Z","signature_b64":"CDHGqQNeK3n4wxQVAu62LOFpApCca7cKc9wTcU9SmSl6lyZIgNeSIOrlXMfOm3cdi9T8BBi+9AqkN4XVTqsHAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f6a2ed017000845f23c18b1ff0247cd539248d555098125d3a3a21524e6141c","last_reissued_at":"2026-07-05T08:38:32.693922Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:32.693922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Antonios Gasteratos, George Pavlidis, Spyridon Mouroutsos, Vasileios Sevetlidis","submitted_at":"2023-03-21T13:48:53Z","abstract_excerpt":"This study proposes a novel approach for solving the PU learning problem based on an anomaly-detection strategy. Latent encodings extracted from positive-labeled data are linearly combined to acquire new samples. These new samples are used as embeddings to increase the density of positive-labeled data and, thus, define a boundary that approximates the positive class. The further a sample is from the boundary the more it is considered as a negative sample. Once a set of negative samples is obtained, the PU learning problem reduces to binary classification. The approach, named Dens-PU due to its"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.11848","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2303.11848/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2303.11848","created_at":"2026-07-05T08:38:32.693977+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.11848v1","created_at":"2026-07-05T08:38:32.693977+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.11848","created_at":"2026-07-05T08:38:32.693977+00:00"},{"alias_kind":"pith_short_12","alias_value":"L5VC5UAXAAEE","created_at":"2026-07-05T08:38:32.693977+00:00"},{"alias_kind":"pith_short_16","alias_value":"L5VC5UAXAAEEL4R4","created_at":"2026-07-05T08:38:32.693977+00:00"},{"alias_kind":"pith_short_8","alias_value":"L5VC5UAX","created_at":"2026-07-05T08:38:32.693977+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/L5VC5UAXAAEEL4R4DCY76ASHZV","json":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV.json","graph_json":"https://pith.science/api/pith-number/L5VC5UAXAAEEL4R4DCY76ASHZV/graph.json","events_json":"https://pith.science/api/pith-number/L5VC5UAXAAEEL4R4DCY76ASHZV/events.json","paper":"https://pith.science/paper/L5VC5UAX"},"agent_actions":{"view_html":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV","download_json":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV.json","view_paper":"https://pith.science/paper/L5VC5UAX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.11848&json=true","fetch_graph":"https://pith.science/api/pith-number/L5VC5UAXAAEEL4R4DCY76ASHZV/graph.json","fetch_events":"https://pith.science/api/pith-number/L5VC5UAXAAEEL4R4DCY76ASHZV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV/action/storage_attestation","attest_author":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV/action/author_attestation","sign_citation":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV/action/citation_signature","submit_replication":"https://pith.science/pith/L5VC5UAXAAEEL4R4DCY76ASHZV/action/replication_record"}},"created_at":"2026-07-05T08:38:32.693977+00:00","updated_at":"2026-07-05T08:38:32.693977+00:00"}