{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HBGDUGFKKWYOCYLJRXOLSRQUWN","short_pith_number":"pith:HBGDUGFK","schema_version":"1.0","canonical_sha256":"384c3a18aa55b0e161698ddcb94614b35110cec5cd3cce82bc6118872f74dc36","source":{"kind":"arxiv","id":"2402.19147","version":1},"attestation_state":"computed","paper":{"title":"Efficient quaternion CUR method for low-rank approximation to quaternion matrix","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Hongmin Cai, Kit Ian Kou, Peng-Ling Wu, Zhaoyuan Yu","submitted_at":"2024-02-29T13:31:08Z","abstract_excerpt":"The low-rank quaternion matrix approximation has been successfully applied in many applications involving signal processing and color image processing. However, the cost of quaternion models for generating low-rank quaternion matrix approximation is sometimes considerable due to the computation of the quaternion singular value decomposition (QSVD), which limits their application to real large-scale data. To address this deficiency, an efficient quaternion matrix CUR (QMCUR) method for low-rank approximation is suggested, which provides significant acceleration in color image processing. We fir"},"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":"2402.19147","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-02-29T13:31:08Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"16e21b3de354d4710bb194746345a652631b556fbf0203bd254b32d5fe3d3280","abstract_canon_sha256":"781635ade13ba58b56b7f183ea18ec23d46a4c8a2bb00dafbc0e92fda2db4333"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:41.656247Z","signature_b64":"GRSn04xY3+DjzIXa45btf7Ny1QchWM6Ecy7nF9kFhxzNJcsEBtc9A7j0QDsY2Qs3UvmBjUjYJzZ2ODvlptK4BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"384c3a18aa55b0e161698ddcb94614b35110cec5cd3cce82bc6118872f74dc36","last_reissued_at":"2026-07-05T07:50:41.655732Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:41.655732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient quaternion CUR method for low-rank approximation to quaternion matrix","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Hongmin Cai, Kit Ian Kou, Peng-Ling Wu, Zhaoyuan Yu","submitted_at":"2024-02-29T13:31:08Z","abstract_excerpt":"The low-rank quaternion matrix approximation has been successfully applied in many applications involving signal processing and color image processing. However, the cost of quaternion models for generating low-rank quaternion matrix approximation is sometimes considerable due to the computation of the quaternion singular value decomposition (QSVD), which limits their application to real large-scale data. To address this deficiency, an efficient quaternion matrix CUR (QMCUR) method for low-rank approximation is suggested, which provides significant acceleration in color image processing. We fir"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.19147","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/2402.19147/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":"2402.19147","created_at":"2026-07-05T07:50:41.655793+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.19147v1","created_at":"2026-07-05T07:50:41.655793+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.19147","created_at":"2026-07-05T07:50:41.655793+00:00"},{"alias_kind":"pith_short_12","alias_value":"HBGDUGFKKWYO","created_at":"2026-07-05T07:50:41.655793+00:00"},{"alias_kind":"pith_short_16","alias_value":"HBGDUGFKKWYOCYLJ","created_at":"2026-07-05T07:50:41.655793+00:00"},{"alias_kind":"pith_short_8","alias_value":"HBGDUGFK","created_at":"2026-07-05T07:50:41.655793+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/HBGDUGFKKWYOCYLJRXOLSRQUWN","json":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN.json","graph_json":"https://pith.science/api/pith-number/HBGDUGFKKWYOCYLJRXOLSRQUWN/graph.json","events_json":"https://pith.science/api/pith-number/HBGDUGFKKWYOCYLJRXOLSRQUWN/events.json","paper":"https://pith.science/paper/HBGDUGFK"},"agent_actions":{"view_html":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN","download_json":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN.json","view_paper":"https://pith.science/paper/HBGDUGFK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.19147&json=true","fetch_graph":"https://pith.science/api/pith-number/HBGDUGFKKWYOCYLJRXOLSRQUWN/graph.json","fetch_events":"https://pith.science/api/pith-number/HBGDUGFKKWYOCYLJRXOLSRQUWN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN/action/storage_attestation","attest_author":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN/action/author_attestation","sign_citation":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN/action/citation_signature","submit_replication":"https://pith.science/pith/HBGDUGFKKWYOCYLJRXOLSRQUWN/action/replication_record"}},"created_at":"2026-07-05T07:50:41.655793+00:00","updated_at":"2026-07-05T07:50:41.655793+00:00"}