{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:A7ASAXLJFMP2GCHEGIO6KLRM3R","short_pith_number":"pith:A7ASAXLJ","schema_version":"1.0","canonical_sha256":"07c1205d692b1fa308e4321de52e2cdc58a6ef84f1e932220e8e2294ddf2fb5e","source":{"kind":"arxiv","id":"1611.09742","version":2},"attestation_state":"computed","paper":{"title":"Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.OC"],"primary_cat":"cs.IT","authors_text":"Mohamed Suliman, Tareq Y. Al-Naffouri, Tarig Ballal","submitted_at":"2016-11-29T17:34:38Z","abstract_excerpt":"Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimate"},"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":"1611.09742","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-11-29T17:34:38Z","cross_cats_sorted":["math.IT","math.OC"],"title_canon_sha256":"cc2df24b498dee2ada9ce4c91b4d970a5e2a6d655bee1bee3862532edfa7e536","abstract_canon_sha256":"7ce253fadc135c7443e605b075aed538c4406e93684e80d9113043ed1480001e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:04.968751Z","signature_b64":"Dss6Qb56vO8VOF8pz8Y4tjtBUAR60yZJFsccQRqcuBj4Nx2LMrca2YasCIJG/eug5amQFT1ecAIm0l8G1AqaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07c1205d692b1fa308e4321de52e2cdc58a6ef84f1e932220e8e2294ddf2fb5e","last_reissued_at":"2026-05-18T00:53:04.968073Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:04.968073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.OC"],"primary_cat":"cs.IT","authors_text":"Mohamed Suliman, Tareq Y. Al-Naffouri, Tarig Ballal","submitted_at":"2016-11-29T17:34:38Z","abstract_excerpt":"Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.09742","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":""},"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":"1611.09742","created_at":"2026-05-18T00:53:04.968184+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.09742v2","created_at":"2026-05-18T00:53:04.968184+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.09742","created_at":"2026-05-18T00:53:04.968184+00:00"},{"alias_kind":"pith_short_12","alias_value":"A7ASAXLJFMP2","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_16","alias_value":"A7ASAXLJFMP2GCHE","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_8","alias_value":"A7ASAXLJ","created_at":"2026-05-18T12:30:04.600751+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/A7ASAXLJFMP2GCHEGIO6KLRM3R","json":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R.json","graph_json":"https://pith.science/api/pith-number/A7ASAXLJFMP2GCHEGIO6KLRM3R/graph.json","events_json":"https://pith.science/api/pith-number/A7ASAXLJFMP2GCHEGIO6KLRM3R/events.json","paper":"https://pith.science/paper/A7ASAXLJ"},"agent_actions":{"view_html":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R","download_json":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R.json","view_paper":"https://pith.science/paper/A7ASAXLJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.09742&json=true","fetch_graph":"https://pith.science/api/pith-number/A7ASAXLJFMP2GCHEGIO6KLRM3R/graph.json","fetch_events":"https://pith.science/api/pith-number/A7ASAXLJFMP2GCHEGIO6KLRM3R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R/action/storage_attestation","attest_author":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R/action/author_attestation","sign_citation":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R/action/citation_signature","submit_replication":"https://pith.science/pith/A7ASAXLJFMP2GCHEGIO6KLRM3R/action/replication_record"}},"created_at":"2026-05-18T00:53:04.968184+00:00","updated_at":"2026-05-18T00:53:04.968184+00:00"}