{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:D2C4VW74WSBEIVSPP4IN4TN3ZP","short_pith_number":"pith:D2C4VW74","schema_version":"1.0","canonical_sha256":"1e85cadbfcb48244564f7f10de4dbbcbd54a49fee5e56eb487f92ddc40aa774b","source":{"kind":"arxiv","id":"1608.08285","version":1},"attestation_state":"computed","paper":{"title":"Integrating multiple random sketches for singular value decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"math.NA","authors_text":"Chienyao Lin, Dawei D. Chang, Hung Chen, Su-Yun Huang, Ting-Li Chen, Weichung Wang","submitted_at":"2016-08-29T23:34:53Z","abstract_excerpt":"The singular value decomposition (SVD) of large-scale matrices is a key tool in data analytics and scientific computing. The rapid growth in the size of matrices further increases the need for developing efficient large-scale SVD algorithms. Randomized SVD based on one-time sketching has been studied, and its potential has been demonstrated for computing a low-rank SVD. Instead of exploring different single random sketching techniques, we propose a Monte Carlo type integrated SVD algorithm based on multiple random sketches. The proposed integration algorithm takes multiple random sketches and "},"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":"1608.08285","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2016-08-29T23:34:53Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"40f44df954b21b72c58279c6b4b01b94c4225f106784513d61c63ba3843d864d","abstract_canon_sha256":"8dcecff3b8bdcfef299a11bb11aaec3b76a8d4e04b28fe2e7812c42d23bb14b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:07:19.784132Z","signature_b64":"dDpvHT3Vc6P4xTfizaV4MYAmNolKio+wtO8LVLVKj5F+XT5vwYdnjSCdttq4N7hxOslf0n9qeOLEEzHF17ABDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e85cadbfcb48244564f7f10de4dbbcbd54a49fee5e56eb487f92ddc40aa774b","last_reissued_at":"2026-05-18T01:07:19.783671Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:07:19.783671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Integrating multiple random sketches for singular value decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"math.NA","authors_text":"Chienyao Lin, Dawei D. Chang, Hung Chen, Su-Yun Huang, Ting-Li Chen, Weichung Wang","submitted_at":"2016-08-29T23:34:53Z","abstract_excerpt":"The singular value decomposition (SVD) of large-scale matrices is a key tool in data analytics and scientific computing. The rapid growth in the size of matrices further increases the need for developing efficient large-scale SVD algorithms. Randomized SVD based on one-time sketching has been studied, and its potential has been demonstrated for computing a low-rank SVD. Instead of exploring different single random sketching techniques, we propose a Monte Carlo type integrated SVD algorithm based on multiple random sketches. The proposed integration algorithm takes multiple random sketches and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.08285","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":"1608.08285","created_at":"2026-05-18T01:07:19.783747+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.08285v1","created_at":"2026-05-18T01:07:19.783747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.08285","created_at":"2026-05-18T01:07:19.783747+00:00"},{"alias_kind":"pith_short_12","alias_value":"D2C4VW74WSBE","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"D2C4VW74WSBEIVSP","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"D2C4VW74","created_at":"2026-05-18T12:30:09.641336+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.19152","citing_title":"Transfer Learning for Degree-Corrected Mixed Membership Network Models","ref_index":68,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP","json":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP.json","graph_json":"https://pith.science/api/pith-number/D2C4VW74WSBEIVSPP4IN4TN3ZP/graph.json","events_json":"https://pith.science/api/pith-number/D2C4VW74WSBEIVSPP4IN4TN3ZP/events.json","paper":"https://pith.science/paper/D2C4VW74"},"agent_actions":{"view_html":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP","download_json":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP.json","view_paper":"https://pith.science/paper/D2C4VW74","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.08285&json=true","fetch_graph":"https://pith.science/api/pith-number/D2C4VW74WSBEIVSPP4IN4TN3ZP/graph.json","fetch_events":"https://pith.science/api/pith-number/D2C4VW74WSBEIVSPP4IN4TN3ZP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP/action/storage_attestation","attest_author":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP/action/author_attestation","sign_citation":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP/action/citation_signature","submit_replication":"https://pith.science/pith/D2C4VW74WSBEIVSPP4IN4TN3ZP/action/replication_record"}},"created_at":"2026-05-18T01:07:19.783747+00:00","updated_at":"2026-05-18T01:07:19.783747+00:00"}