{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:73LRH4PQS3P76WYK5JPGNSACUY","short_pith_number":"pith:73LRH4PQ","schema_version":"1.0","canonical_sha256":"fed713f1f096dfff5b0aea5e66c802a62d93068be8d011685771480e4b56043c","source":{"kind":"arxiv","id":"1904.04081","version":1},"attestation_state":"computed","paper":{"title":"Heterogeneous Multi-task Metric Learning across Multiple Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dacheng Tao, Yonggang Wen, Yong Luo","submitted_at":"2019-04-08T13:59:36Z","abstract_excerpt":"Distance metric learning (DML) plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multi-task metric learning (MTML), which can be regarded as a special case of TML, performs transfer across all related domains. Current TML tools usually assume that the same feature representation is exploited for different domains. However, in real-world applications, data may be drawn from heterogeneou"},"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":"1904.04081","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-04-08T13:59:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d59de8e5236ed7ff91a491965142fe6d728f75c2d952289cc5817544a228a034","abstract_canon_sha256":"d6fd35a816b5e274831edacee2d42e357d2daf9f78ab42982f94dfcb34689519"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:07.140149Z","signature_b64":"/PPRMfDOlkf13Bfo2t81ixETYgSF5c82sVoelcCOavsHrnBM3I4EtQMW5o9zSY6GUcytjB9a3uBRF2fwrbpcCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fed713f1f096dfff5b0aea5e66c802a62d93068be8d011685771480e4b56043c","last_reissued_at":"2026-05-17T23:49:07.139623Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:07.139623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Heterogeneous Multi-task Metric Learning across Multiple Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dacheng Tao, Yonggang Wen, Yong Luo","submitted_at":"2019-04-08T13:59:36Z","abstract_excerpt":"Distance metric learning (DML) plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in target domain is limited, transfer metric learning (TML) helps to learn the metric by leveraging the sufficient information from other related domains. Multi-task metric learning (MTML), which can be regarded as a special case of TML, performs transfer across all related domains. Current TML tools usually assume that the same feature representation is exploited for different domains. However, in real-world applications, data may be drawn from heterogeneou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04081","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":"1904.04081","created_at":"2026-05-17T23:49:07.139710+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.04081v1","created_at":"2026-05-17T23:49:07.139710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04081","created_at":"2026-05-17T23:49:07.139710+00:00"},{"alias_kind":"pith_short_12","alias_value":"73LRH4PQS3P7","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"73LRH4PQS3P76WYK","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"73LRH4PQ","created_at":"2026-05-18T12:33:10.108867+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/73LRH4PQS3P76WYK5JPGNSACUY","json":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY.json","graph_json":"https://pith.science/api/pith-number/73LRH4PQS3P76WYK5JPGNSACUY/graph.json","events_json":"https://pith.science/api/pith-number/73LRH4PQS3P76WYK5JPGNSACUY/events.json","paper":"https://pith.science/paper/73LRH4PQ"},"agent_actions":{"view_html":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY","download_json":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY.json","view_paper":"https://pith.science/paper/73LRH4PQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.04081&json=true","fetch_graph":"https://pith.science/api/pith-number/73LRH4PQS3P76WYK5JPGNSACUY/graph.json","fetch_events":"https://pith.science/api/pith-number/73LRH4PQS3P76WYK5JPGNSACUY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY/action/storage_attestation","attest_author":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY/action/author_attestation","sign_citation":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY/action/citation_signature","submit_replication":"https://pith.science/pith/73LRH4PQS3P76WYK5JPGNSACUY/action/replication_record"}},"created_at":"2026-05-17T23:49:07.139710+00:00","updated_at":"2026-05-17T23:49:07.139710+00:00"}