{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4OWPS2WYVK5ACTA4743BYG7VBS","short_pith_number":"pith:4OWPS2WY","schema_version":"1.0","canonical_sha256":"e3acf96ad8aaba014c1cff361c1bf50cbe9cbc3c28dc2a9f03a603a56102e1b0","source":{"kind":"arxiv","id":"1901.00563","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Locality Preserving Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jie Wen, Lunke Fei, Runze Chen, Zheng Zhang, Zhihui Lai, Zuofeng Zhong","submitted_at":"2019-01-03T00:36:23Z","abstract_excerpt":"This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by t"},"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":"1901.00563","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-03T00:36:23Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"db98c43696b0473e4e2c80494cc95def2302ef90c2ecb9858a674512e60772bb","abstract_canon_sha256":"3f0fa469c844434ebaa70ec2bd3fa814602869e0b3574362397d5f0cb6bb3341"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:02.150633Z","signature_b64":"pDS1PwdAJMQneoGs/y1NOgD8sRfz+f7IZfckodR71tCuHmYOPbdRkctqzyX9qzVxNKRgS+euGkMwqy1oZFAYDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3acf96ad8aaba014c1cff361c1bf50cbe9cbc3c28dc2a9f03a603a56102e1b0","last_reissued_at":"2026-05-17T23:57:02.149930Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:02.149930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Locality Preserving Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jie Wen, Lunke Fei, Runze Chen, Zheng Zhang, Zhihui Lai, Zuofeng Zhong","submitted_at":"2019-01-03T00:36:23Z","abstract_excerpt":"This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.00563","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":"1901.00563","created_at":"2026-05-17T23:57:02.150038+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.00563v1","created_at":"2026-05-17T23:57:02.150038+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.00563","created_at":"2026-05-17T23:57:02.150038+00:00"},{"alias_kind":"pith_short_12","alias_value":"4OWPS2WYVK5A","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"4OWPS2WYVK5ACTA4","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"4OWPS2WY","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/4OWPS2WYVK5ACTA4743BYG7VBS","json":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS.json","graph_json":"https://pith.science/api/pith-number/4OWPS2WYVK5ACTA4743BYG7VBS/graph.json","events_json":"https://pith.science/api/pith-number/4OWPS2WYVK5ACTA4743BYG7VBS/events.json","paper":"https://pith.science/paper/4OWPS2WY"},"agent_actions":{"view_html":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS","download_json":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS.json","view_paper":"https://pith.science/paper/4OWPS2WY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.00563&json=true","fetch_graph":"https://pith.science/api/pith-number/4OWPS2WYVK5ACTA4743BYG7VBS/graph.json","fetch_events":"https://pith.science/api/pith-number/4OWPS2WYVK5ACTA4743BYG7VBS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS/action/storage_attestation","attest_author":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS/action/author_attestation","sign_citation":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS/action/citation_signature","submit_replication":"https://pith.science/pith/4OWPS2WYVK5ACTA4743BYG7VBS/action/replication_record"}},"created_at":"2026-05-17T23:57:02.150038+00:00","updated_at":"2026-05-17T23:57:02.150038+00:00"}