{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RO2D5R2PPVROXO23HH33DA3HM5","short_pith_number":"pith:RO2D5R2P","schema_version":"1.0","canonical_sha256":"8bb43ec74f7d62ebbb5b39f7b18367676437774d568e28047988fcc25c7eff7b","source":{"kind":"arxiv","id":"1908.07878","version":1},"attestation_state":"computed","paper":{"title":"Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Guangcan Liu, Meng Wang, Yang Wang, Yulin Sun, Zhao Zhang, Zheng Zhang","submitted_at":"2019-08-21T13:59:00Z","abstract_excerpt":"In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unif"},"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":"1908.07878","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-21T13:59:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"52147f42c5826f29ef6b39b6eac70527dd7e296eb8b5ba94a22dddd69fbfedf3","abstract_canon_sha256":"d4c28f12cf11bfd6c3fcc2aca159204c05cdc4f93309396154bebb6681a6036e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:02.057587Z","signature_b64":"5wsc5g7Fa66BZbMuukEZ55iNDyGEG6bWxNRSDnDpg6jrfewz7hEmmYLtK6miIKXf+izZBY9ASeESgfe0ykIxAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8bb43ec74f7d62ebbb5b39f7b18367676437774d568e28047988fcc25c7eff7b","last_reissued_at":"2026-07-04T23:59:02.057229Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:02.057229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Guangcan Liu, Meng Wang, Yang Wang, Yulin Sun, Zhao Zhang, Zheng Zhang","submitted_at":"2019-08-21T13:59:00Z","abstract_excerpt":"In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07878","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/1908.07878/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":"1908.07878","created_at":"2026-07-04T23:59:02.057284+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.07878v1","created_at":"2026-07-04T23:59:02.057284+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07878","created_at":"2026-07-04T23:59:02.057284+00:00"},{"alias_kind":"pith_short_12","alias_value":"RO2D5R2PPVRO","created_at":"2026-07-04T23:59:02.057284+00:00"},{"alias_kind":"pith_short_16","alias_value":"RO2D5R2PPVROXO23","created_at":"2026-07-04T23:59:02.057284+00:00"},{"alias_kind":"pith_short_8","alias_value":"RO2D5R2P","created_at":"2026-07-04T23:59:02.057284+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/RO2D5R2PPVROXO23HH33DA3HM5","json":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5.json","graph_json":"https://pith.science/api/pith-number/RO2D5R2PPVROXO23HH33DA3HM5/graph.json","events_json":"https://pith.science/api/pith-number/RO2D5R2PPVROXO23HH33DA3HM5/events.json","paper":"https://pith.science/paper/RO2D5R2P"},"agent_actions":{"view_html":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5","download_json":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5.json","view_paper":"https://pith.science/paper/RO2D5R2P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.07878&json=true","fetch_graph":"https://pith.science/api/pith-number/RO2D5R2PPVROXO23HH33DA3HM5/graph.json","fetch_events":"https://pith.science/api/pith-number/RO2D5R2PPVROXO23HH33DA3HM5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5/action/storage_attestation","attest_author":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5/action/author_attestation","sign_citation":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5/action/citation_signature","submit_replication":"https://pith.science/pith/RO2D5R2PPVROXO23HH33DA3HM5/action/replication_record"}},"created_at":"2026-07-04T23:59:02.057284+00:00","updated_at":"2026-07-04T23:59:02.057284+00:00"}