{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PJZDMQ3WTF63ENMBZUEUFIJT3S","short_pith_number":"pith:PJZDMQ3W","schema_version":"1.0","canonical_sha256":"7a72364376997db23581cd0942a133dc999465469276b572e6c822076e2818f7","source":{"kind":"arxiv","id":"1906.04598","version":1},"attestation_state":"computed","paper":{"title":"Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jiahuan Ren, Meng Wang, Richang Hong, Shuicheng Yan, Weiming Jiang, Zhao Zhang, Zheng Zhang","submitted_at":"2019-06-11T13:49:06Z","abstract_excerpt":"We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL). RFDDL mainly improves the data representation and classification abilities by enhancing the robust property to sparse errors and encoding the locality, reconstruction error and label consistency more accurately. First, for the robustness to noise and sparse errors in data and atoms, RFDDL aims at recovering the underlying clean data and clean atom subspaces jointly, and then performs DL and encodes the lo"},"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":"1906.04598","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-11T13:49:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2b878761296ca82686ea5b7fd97c60fe5f26dc0a040d7ae3b13bba16472b30a0","abstract_canon_sha256":"0e48114e6f2f5100327a63ea601997e52436e67ad6270935ed93b41797f72dab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:38.804085Z","signature_b64":"dpNX40tr/mm+NFtQSRT4N9M6SezbChYY10XTTdbFH68BdCf92hmh/zeiXZjAnZnIoBkknXHsPx1ziRbDxTeFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a72364376997db23581cd0942a133dc999465469276b572e6c822076e2818f7","last_reissued_at":"2026-05-17T23:43:38.803482Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:38.803482Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Jiahuan Ren, Meng Wang, Richang Hong, Shuicheng Yan, Weiming Jiang, Zhao Zhang, Zheng Zhang","submitted_at":"2019-06-11T13:49:06Z","abstract_excerpt":"We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL). RFDDL mainly improves the data representation and classification abilities by enhancing the robust property to sparse errors and encoding the locality, reconstruction error and label consistency more accurately. First, for the robustness to noise and sparse errors in data and atoms, RFDDL aims at recovering the underlying clean data and clean atom subspaces jointly, and then performs DL and encodes the lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04598","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":"1906.04598","created_at":"2026-05-17T23:43:38.803573+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04598v1","created_at":"2026-05-17T23:43:38.803573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04598","created_at":"2026-05-17T23:43:38.803573+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJZDMQ3WTF63","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJZDMQ3WTF63ENMB","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJZDMQ3W","created_at":"2026-05-18T12:33:24.271573+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/PJZDMQ3WTF63ENMBZUEUFIJT3S","json":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S.json","graph_json":"https://pith.science/api/pith-number/PJZDMQ3WTF63ENMBZUEUFIJT3S/graph.json","events_json":"https://pith.science/api/pith-number/PJZDMQ3WTF63ENMBZUEUFIJT3S/events.json","paper":"https://pith.science/paper/PJZDMQ3W"},"agent_actions":{"view_html":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S","download_json":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S.json","view_paper":"https://pith.science/paper/PJZDMQ3W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04598&json=true","fetch_graph":"https://pith.science/api/pith-number/PJZDMQ3WTF63ENMBZUEUFIJT3S/graph.json","fetch_events":"https://pith.science/api/pith-number/PJZDMQ3WTF63ENMBZUEUFIJT3S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S/action/storage_attestation","attest_author":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S/action/author_attestation","sign_citation":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S/action/citation_signature","submit_replication":"https://pith.science/pith/PJZDMQ3WTF63ENMBZUEUFIJT3S/action/replication_record"}},"created_at":"2026-05-17T23:43:38.803573+00:00","updated_at":"2026-05-17T23:43:38.803573+00:00"}