{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:K5J2IQHIPXQF7JGNLYWENYTV2E","short_pith_number":"pith:K5J2IQHI","schema_version":"1.0","canonical_sha256":"5753a440e87de05fa4cd5e2c46e275d12ebdabab7d2b117f8ff3c57a80647390","source":{"kind":"arxiv","id":"2412.10005","version":2},"attestation_state":"computed","paper":{"title":"Matrix Completion via Residual Spectral Matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Fang Yao, Ziyuan Chen","submitted_at":"2024-12-13T09:42:42Z","abstract_excerpt":"Noisy matrix completion has attracted significant attention due to its applications in recommendation systems, signal processing and image restoration. Most existing works rely on (weighted) least squares methods under various low-rank constraints. However, minimizing the sum of squared residuals is not always efficient, as it may ignore the potential structural information in the residuals. In this study, we propose a novel residual spectral matching criterion that incorporates not only the numerical but also locational information of residuals. This criterion is the first in noisy matrix com"},"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":"2412.10005","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-12-13T09:42:42Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"7b68c7fe4406859d5e48d2d5051643b87d4e19fee2c8421df088e293ef371432","abstract_canon_sha256":"a6bf25e5ebc6efdcdbd2ee3bc4b0c2ed998930a99af55eeb522bb1bcd53167bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:14.704901Z","signature_b64":"f0IUJFdDd0yuEqYptb2Qs10vanQxVJdYWj+n7TDW2uw9glAReOrPIhm7leW2Kon48Eo+1zgW8DdzwuPpiUtZBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5753a440e87de05fa4cd5e2c46e275d12ebdabab7d2b117f8ff3c57a80647390","last_reissued_at":"2026-07-05T09:49:14.704231Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:14.704231Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Matrix Completion via Residual Spectral Matching","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Fang Yao, Ziyuan Chen","submitted_at":"2024-12-13T09:42:42Z","abstract_excerpt":"Noisy matrix completion has attracted significant attention due to its applications in recommendation systems, signal processing and image restoration. Most existing works rely on (weighted) least squares methods under various low-rank constraints. However, minimizing the sum of squared residuals is not always efficient, as it may ignore the potential structural information in the residuals. In this study, we propose a novel residual spectral matching criterion that incorporates not only the numerical but also locational information of residuals. This criterion is the first in noisy matrix com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.10005","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/2412.10005/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":"2412.10005","created_at":"2026-07-05T09:49:14.704326+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.10005v2","created_at":"2026-07-05T09:49:14.704326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.10005","created_at":"2026-07-05T09:49:14.704326+00:00"},{"alias_kind":"pith_short_12","alias_value":"K5J2IQHIPXQF","created_at":"2026-07-05T09:49:14.704326+00:00"},{"alias_kind":"pith_short_16","alias_value":"K5J2IQHIPXQF7JGN","created_at":"2026-07-05T09:49:14.704326+00:00"},{"alias_kind":"pith_short_8","alias_value":"K5J2IQHI","created_at":"2026-07-05T09:49:14.704326+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/K5J2IQHIPXQF7JGNLYWENYTV2E","json":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E.json","graph_json":"https://pith.science/api/pith-number/K5J2IQHIPXQF7JGNLYWENYTV2E/graph.json","events_json":"https://pith.science/api/pith-number/K5J2IQHIPXQF7JGNLYWENYTV2E/events.json","paper":"https://pith.science/paper/K5J2IQHI"},"agent_actions":{"view_html":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E","download_json":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E.json","view_paper":"https://pith.science/paper/K5J2IQHI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.10005&json=true","fetch_graph":"https://pith.science/api/pith-number/K5J2IQHIPXQF7JGNLYWENYTV2E/graph.json","fetch_events":"https://pith.science/api/pith-number/K5J2IQHIPXQF7JGNLYWENYTV2E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E/action/storage_attestation","attest_author":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E/action/author_attestation","sign_citation":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E/action/citation_signature","submit_replication":"https://pith.science/pith/K5J2IQHIPXQF7JGNLYWENYTV2E/action/replication_record"}},"created_at":"2026-07-05T09:49:14.704326+00:00","updated_at":"2026-07-05T09:49:14.704326+00:00"}