{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:N2GTFBDLIMSIJJNUYGUH4FDRLB","short_pith_number":"pith:N2GTFBDL","schema_version":"1.0","canonical_sha256":"6e8d32846b432484a5b4c1a87e1471586ef1ff65f3f2ed6289481bb60efb7526","source":{"kind":"arxiv","id":"2408.08260","version":2},"attestation_state":"computed","paper":{"title":"GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Timothy E. Holy, Youdong Guo","submitted_at":"2024-08-15T17:01:00Z","abstract_excerpt":"Non-negative matrix factorization (NMF) is an important tool in signal processing and widely used to separate mixed sources into their components. Algorithms for NMF require that the user choose the number of components in advance, and if the results are unsatisfying one typically needs to start again with a different number of components. To make NMF more interactive and incremental, here we introduce GSVD-NMF, a method that proposes new components based on the generalized singular value decomposition (GSVD) to address discrepancies between the initial under-complete NMF results and the SVD o"},"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":"2408.08260","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-08-15T17:01:00Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"f6ede92a73978f945ab523ce2b28f0ab19672eaad61c57cd33060d6d43223340","abstract_canon_sha256":"d28f19100f45ce42189e60d1f1ebaf3fb2162dfedd354764aff5e9ed7c8158f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:58:42.284698Z","signature_b64":"T4icykm19DwTzszyrqpYJqATowPlBKumKfIdxvmXMKYIp9Zu76L1Gy7Q0XEeYDrApcaUXKTWpJEfoBUlHtWyBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e8d32846b432484a5b4c1a87e1471586ef1ff65f3f2ed6289481bb60efb7526","last_reissued_at":"2026-07-05T09:58:42.284284Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:58:42.284284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Timothy E. Holy, Youdong Guo","submitted_at":"2024-08-15T17:01:00Z","abstract_excerpt":"Non-negative matrix factorization (NMF) is an important tool in signal processing and widely used to separate mixed sources into their components. Algorithms for NMF require that the user choose the number of components in advance, and if the results are unsatisfying one typically needs to start again with a different number of components. To make NMF more interactive and incremental, here we introduce GSVD-NMF, a method that proposes new components based on the generalized singular value decomposition (GSVD) to address discrepancies between the initial under-complete NMF results and the SVD o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08260","kind":"arxiv","version":2},"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/2408.08260/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":"2408.08260","created_at":"2026-07-05T09:58:42.284339+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.08260v2","created_at":"2026-07-05T09:58:42.284339+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08260","created_at":"2026-07-05T09:58:42.284339+00:00"},{"alias_kind":"pith_short_12","alias_value":"N2GTFBDLIMSI","created_at":"2026-07-05T09:58:42.284339+00:00"},{"alias_kind":"pith_short_16","alias_value":"N2GTFBDLIMSIJJNU","created_at":"2026-07-05T09:58:42.284339+00:00"},{"alias_kind":"pith_short_8","alias_value":"N2GTFBDL","created_at":"2026-07-05T09:58:42.284339+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/N2GTFBDLIMSIJJNUYGUH4FDRLB","json":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB.json","graph_json":"https://pith.science/api/pith-number/N2GTFBDLIMSIJJNUYGUH4FDRLB/graph.json","events_json":"https://pith.science/api/pith-number/N2GTFBDLIMSIJJNUYGUH4FDRLB/events.json","paper":"https://pith.science/paper/N2GTFBDL"},"agent_actions":{"view_html":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB","download_json":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB.json","view_paper":"https://pith.science/paper/N2GTFBDL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.08260&json=true","fetch_graph":"https://pith.science/api/pith-number/N2GTFBDLIMSIJJNUYGUH4FDRLB/graph.json","fetch_events":"https://pith.science/api/pith-number/N2GTFBDLIMSIJJNUYGUH4FDRLB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB/action/storage_attestation","attest_author":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB/action/author_attestation","sign_citation":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB/action/citation_signature","submit_replication":"https://pith.science/pith/N2GTFBDLIMSIJJNUYGUH4FDRLB/action/replication_record"}},"created_at":"2026-07-05T09:58:42.284339+00:00","updated_at":"2026-07-05T09:58:42.284339+00:00"}