{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:SFKZZSUVQGNS6T2SY6FUDOKCWW","short_pith_number":"pith:SFKZZSUV","schema_version":"1.0","canonical_sha256":"91559cca95819b2f4f52c78b41b942b588a57bd8dfc0a244b47078f1a9917247","source":{"kind":"arxiv","id":"1301.0339","version":1},"attestation_state":"computed","paper":{"title":"A Geometric Blind Source Separation Method Based on Facet Component Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.NA","authors_text":"J. Xin, P. Yin, Y. Sun","submitted_at":"2013-01-02T21:58:03Z","abstract_excerpt":"Given a set of mixtures, blind source separation attempts to retrieve the source signals without or with very little information of the the mixing process. We present a geometric approach for blind separation of nonnegative linear mixtures termed {\\em facet component analysis} (FCA). The approach is based on facet identification of the underlying cone structure of the data. Earlier works focus on recovering the cone by locating its vertices (vertex component analysis or VCA) based on a mutual sparsity condition which requires each source signal to possess a stand-alone peak in its spectrum. We"},"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":"1301.0339","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"math.NA","submitted_at":"2013-01-02T21:58:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ab2f8fc43f1ee73d56c6a9db7060ed297027ad63edfdac558e0819b70d82cc17","abstract_canon_sha256":"92ebf3250de6b0716ad3cac4e94d6e61ff8cd9b5e51f120294cb7185a3c95db5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:37:19.817181Z","signature_b64":"stZMSHju7oeUNsKsDHfo+M1GX12LTHdAAE/8Ce9kglM7Ep1TeGraa5j+qQIvf3j5sG0ooolXHe07bjRYFqaqAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91559cca95819b2f4f52c78b41b942b588a57bd8dfc0a244b47078f1a9917247","last_reissued_at":"2026-05-18T03:37:19.816756Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:37:19.816756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Geometric Blind Source Separation Method Based on Facet Component Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.NA","authors_text":"J. Xin, P. Yin, Y. Sun","submitted_at":"2013-01-02T21:58:03Z","abstract_excerpt":"Given a set of mixtures, blind source separation attempts to retrieve the source signals without or with very little information of the the mixing process. We present a geometric approach for blind separation of nonnegative linear mixtures termed {\\em facet component analysis} (FCA). The approach is based on facet identification of the underlying cone structure of the data. Earlier works focus on recovering the cone by locating its vertices (vertex component analysis or VCA) based on a mutual sparsity condition which requires each source signal to possess a stand-alone peak in its spectrum. We"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.0339","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":"1301.0339","created_at":"2026-05-18T03:37:19.816816+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.0339v1","created_at":"2026-05-18T03:37:19.816816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.0339","created_at":"2026-05-18T03:37:19.816816+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFKZZSUVQGNS","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFKZZSUVQGNS6T2S","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFKZZSUV","created_at":"2026-05-18T12:27:59.945178+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/SFKZZSUVQGNS6T2SY6FUDOKCWW","json":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW.json","graph_json":"https://pith.science/api/pith-number/SFKZZSUVQGNS6T2SY6FUDOKCWW/graph.json","events_json":"https://pith.science/api/pith-number/SFKZZSUVQGNS6T2SY6FUDOKCWW/events.json","paper":"https://pith.science/paper/SFKZZSUV"},"agent_actions":{"view_html":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW","download_json":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW.json","view_paper":"https://pith.science/paper/SFKZZSUV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.0339&json=true","fetch_graph":"https://pith.science/api/pith-number/SFKZZSUVQGNS6T2SY6FUDOKCWW/graph.json","fetch_events":"https://pith.science/api/pith-number/SFKZZSUVQGNS6T2SY6FUDOKCWW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW/action/storage_attestation","attest_author":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW/action/author_attestation","sign_citation":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW/action/citation_signature","submit_replication":"https://pith.science/pith/SFKZZSUVQGNS6T2SY6FUDOKCWW/action/replication_record"}},"created_at":"2026-05-18T03:37:19.816816+00:00","updated_at":"2026-05-18T03:37:19.816816+00:00"}