{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:C2RMPELIJUDQGYJ74PEYJYNMDY","short_pith_number":"pith:C2RMPELI","schema_version":"1.0","canonical_sha256":"16a2c791684d0703613fe3c984e1ac1e10ad857eb882e6c20451f63254c6ec03","source":{"kind":"arxiv","id":"1811.10443","version":1},"attestation_state":"computed","paper":{"title":"Sparse spectral estimation with missing and corrupted measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Andreas Elsener, Sara van de Geer","submitted_at":"2018-11-26T15:24:31Z","abstract_excerpt":"Supervised learning methods with missing data have been extensively studied not just due to the techniques related to low-rank matrix completion. Also in unsupervised learning one often relies on imputation methods. As a matter of fact, missing values induce a bias in various estimators such as the sample covariance matrix. In the present paper, a convex method for sparse subspace estimation is extended to the case of missing and corrupted measurements. This is done by correcting the bias instead of imputing the missing values. The estimator is then used as an initial value for a nonconvex pro"},"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":"1811.10443","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-11-26T15:24:31Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"07e0156b8599fe7a7916b3c864283cb250a730268d639fc9333483e78a2a2ee9","abstract_canon_sha256":"e780350303d0e6ddb52afb3f15fbb9cbde31dac19d2ef51501de180f99f6bcd3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:56.438050Z","signature_b64":"m5Ww3+fuE0shzvFbY+inpDCKZ0J3WNGVBgA+g30i0iXeGjF4nwdxCrXrfDqUHSFNOWCVDWWGzDrPH3TawVcQCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16a2c791684d0703613fe3c984e1ac1e10ad857eb882e6c20451f63254c6ec03","last_reissued_at":"2026-05-17T23:59:56.437576Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:56.437576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse spectral estimation with missing and corrupted measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Andreas Elsener, Sara van de Geer","submitted_at":"2018-11-26T15:24:31Z","abstract_excerpt":"Supervised learning methods with missing data have been extensively studied not just due to the techniques related to low-rank matrix completion. Also in unsupervised learning one often relies on imputation methods. As a matter of fact, missing values induce a bias in various estimators such as the sample covariance matrix. In the present paper, a convex method for sparse subspace estimation is extended to the case of missing and corrupted measurements. This is done by correcting the bias instead of imputing the missing values. The estimator is then used as an initial value for a nonconvex pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.10443","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":"1811.10443","created_at":"2026-05-17T23:59:56.437654+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.10443v1","created_at":"2026-05-17T23:59:56.437654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.10443","created_at":"2026-05-17T23:59:56.437654+00:00"},{"alias_kind":"pith_short_12","alias_value":"C2RMPELIJUDQ","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"C2RMPELIJUDQGYJ7","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"C2RMPELI","created_at":"2026-05-18T12:32:16.446611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.12125","citing_title":"High-dimensional principal component analysis with heterogeneous missingness","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY","json":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY.json","graph_json":"https://pith.science/api/pith-number/C2RMPELIJUDQGYJ74PEYJYNMDY/graph.json","events_json":"https://pith.science/api/pith-number/C2RMPELIJUDQGYJ74PEYJYNMDY/events.json","paper":"https://pith.science/paper/C2RMPELI"},"agent_actions":{"view_html":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY","download_json":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY.json","view_paper":"https://pith.science/paper/C2RMPELI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.10443&json=true","fetch_graph":"https://pith.science/api/pith-number/C2RMPELIJUDQGYJ74PEYJYNMDY/graph.json","fetch_events":"https://pith.science/api/pith-number/C2RMPELIJUDQGYJ74PEYJYNMDY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY/action/storage_attestation","attest_author":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY/action/author_attestation","sign_citation":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY/action/citation_signature","submit_replication":"https://pith.science/pith/C2RMPELIJUDQGYJ74PEYJYNMDY/action/replication_record"}},"created_at":"2026-05-17T23:59:56.437654+00:00","updated_at":"2026-05-17T23:59:56.437654+00:00"}