{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VYWHOS4BW7F2MVQ5TS6EJ2T2OZ","short_pith_number":"pith:VYWHOS4B","schema_version":"1.0","canonical_sha256":"ae2c774b81b7cba6561d9cbc44ea7a765b2510220e00f161808037c0e75d53a1","source":{"kind":"arxiv","id":"2305.02544","version":1},"attestation_state":"computed","paper":{"title":"Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Ankit Pensia, Daniel M. Kane, Ilias Diakonikolas, Thanasis Pittas","submitted_at":"2023-05-04T04:45:16Z","abstract_excerpt":"We study principal component analysis (PCA), where given a dataset in $\\mathbb{R}^d$ from a distribution, the task is to find a unit vector $v$ that approximately maximizes the variance of the distribution after being projected along $v$. Despite being a classical task, standard estimators fail drastically if the data contains even a small fraction of outliers, motivating the problem of robust PCA. Recent work has developed computationally-efficient algorithms for robust PCA that either take super-linear time or have sub-optimal error guarantees. Our main contribution is to develop a nearly-li"},"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":"2305.02544","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-04T04:45:16Z","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"93d14ba3d6efd876f641fb42c336238bae177f7c9b0cd321ebfc1d9eeae80362","abstract_canon_sha256":"b7188d6f36eac0aabc68229bcdb923ef1340de561a5da6ae6ac6669fdaad5a14"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:07:02.276545Z","signature_b64":"SFz/gImddO0ukIGd64I3l+IpiaGaC7fxA6RraVbtuHcLNIdQrwY9Hvfnp5krRmMzI87KO7YKADASsg+U6a+WAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae2c774b81b7cba6561d9cbc44ea7a765b2510220e00f161808037c0e75d53a1","last_reissued_at":"2026-07-05T06:07:02.276083Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:07:02.276083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Ankit Pensia, Daniel M. Kane, Ilias Diakonikolas, Thanasis Pittas","submitted_at":"2023-05-04T04:45:16Z","abstract_excerpt":"We study principal component analysis (PCA), where given a dataset in $\\mathbb{R}^d$ from a distribution, the task is to find a unit vector $v$ that approximately maximizes the variance of the distribution after being projected along $v$. Despite being a classical task, standard estimators fail drastically if the data contains even a small fraction of outliers, motivating the problem of robust PCA. Recent work has developed computationally-efficient algorithms for robust PCA that either take super-linear time or have sub-optimal error guarantees. Our main contribution is to develop a nearly-li"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.02544","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2305.02544/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":"2305.02544","created_at":"2026-07-05T06:07:02.276139+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.02544v1","created_at":"2026-07-05T06:07:02.276139+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.02544","created_at":"2026-07-05T06:07:02.276139+00:00"},{"alias_kind":"pith_short_12","alias_value":"VYWHOS4BW7F2","created_at":"2026-07-05T06:07:02.276139+00:00"},{"alias_kind":"pith_short_16","alias_value":"VYWHOS4BW7F2MVQ5","created_at":"2026-07-05T06:07:02.276139+00:00"},{"alias_kind":"pith_short_8","alias_value":"VYWHOS4B","created_at":"2026-07-05T06:07:02.276139+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/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ","json":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ.json","graph_json":"https://pith.science/api/pith-number/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/graph.json","events_json":"https://pith.science/api/pith-number/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/events.json","paper":"https://pith.science/paper/VYWHOS4B"},"agent_actions":{"view_html":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ","download_json":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ.json","view_paper":"https://pith.science/paper/VYWHOS4B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.02544&json=true","fetch_graph":"https://pith.science/api/pith-number/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/graph.json","fetch_events":"https://pith.science/api/pith-number/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/action/storage_attestation","attest_author":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/action/author_attestation","sign_citation":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/action/citation_signature","submit_replication":"https://pith.science/pith/VYWHOS4BW7F2MVQ5TS6EJ2T2OZ/action/replication_record"}},"created_at":"2026-07-05T06:07:02.276139+00:00","updated_at":"2026-07-05T06:07:02.276139+00:00"}