{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:J2CXH6XTUNBYJDQ6RDC4T7GVZS","short_pith_number":"pith:J2CXH6XT","schema_version":"1.0","canonical_sha256":"4e8573faf3a343848e1e88c5c9fcd5cca9407c6bed5ba7df62e20d8d5e34f3d4","source":{"kind":"arxiv","id":"1512.02188","version":2},"attestation_state":"computed","paper":{"title":"Pseudo-Bayesian Robust PCA: Algorithms and Analyses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"David Wipf, In So Kweon, Tae-Hyun Oh, Yasuyuki Matsushita","submitted_at":"2015-12-07T19:43:54Z","abstract_excerpt":"Commonly used in computer vision and other applications, robust PCA represents an algorithmic attempt to reduce the sensitivity of classical PCA to outliers. The basic idea is to learn a decomposition of some data matrix of interest into low rank and sparse components, the latter representing unwanted outliers. Although the resulting optimization problem is typically NP-hard, convex relaxations provide a computationally-expedient alternative with theoretical support. However, in practical regimes performance guarantees break down and a variety of non-convex alternatives, including Bayesian-ins"},"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":"1512.02188","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-12-07T19:43:54Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1d172f6080f56e5548e2c06a90ff826b3c2902890784e303a74b8eae0476a51c","abstract_canon_sha256":"918e587eb16e1ba527bdddff63d323f48506c3742355dbf0ac124fa363e14ebf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:02:59.272242Z","signature_b64":"ftTy8T36zynd8/ZSL44YswQuT3Dt83zaHEntOH1s9ti/kmJlrchxEkV0I26E72EQ8x8lIzHBUfWS3hLqWXImAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e8573faf3a343848e1e88c5c9fcd5cca9407c6bed5ba7df62e20d8d5e34f3d4","last_reissued_at":"2026-05-18T01:02:59.271628Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:02:59.271628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pseudo-Bayesian Robust PCA: Algorithms and Analyses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"David Wipf, In So Kweon, Tae-Hyun Oh, Yasuyuki Matsushita","submitted_at":"2015-12-07T19:43:54Z","abstract_excerpt":"Commonly used in computer vision and other applications, robust PCA represents an algorithmic attempt to reduce the sensitivity of classical PCA to outliers. The basic idea is to learn a decomposition of some data matrix of interest into low rank and sparse components, the latter representing unwanted outliers. Although the resulting optimization problem is typically NP-hard, convex relaxations provide a computationally-expedient alternative with theoretical support. However, in practical regimes performance guarantees break down and a variety of non-convex alternatives, including Bayesian-ins"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.02188","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":"1512.02188","created_at":"2026-05-18T01:02:59.271741+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.02188v2","created_at":"2026-05-18T01:02:59.271741+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.02188","created_at":"2026-05-18T01:02:59.271741+00:00"},{"alias_kind":"pith_short_12","alias_value":"J2CXH6XTUNBY","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_16","alias_value":"J2CXH6XTUNBYJDQ6","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_8","alias_value":"J2CXH6XT","created_at":"2026-05-18T12:29:27.538025+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/J2CXH6XTUNBYJDQ6RDC4T7GVZS","json":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS.json","graph_json":"https://pith.science/api/pith-number/J2CXH6XTUNBYJDQ6RDC4T7GVZS/graph.json","events_json":"https://pith.science/api/pith-number/J2CXH6XTUNBYJDQ6RDC4T7GVZS/events.json","paper":"https://pith.science/paper/J2CXH6XT"},"agent_actions":{"view_html":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS","download_json":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS.json","view_paper":"https://pith.science/paper/J2CXH6XT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.02188&json=true","fetch_graph":"https://pith.science/api/pith-number/J2CXH6XTUNBYJDQ6RDC4T7GVZS/graph.json","fetch_events":"https://pith.science/api/pith-number/J2CXH6XTUNBYJDQ6RDC4T7GVZS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS/action/storage_attestation","attest_author":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS/action/author_attestation","sign_citation":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS/action/citation_signature","submit_replication":"https://pith.science/pith/J2CXH6XTUNBYJDQ6RDC4T7GVZS/action/replication_record"}},"created_at":"2026-05-18T01:02:59.271741+00:00","updated_at":"2026-05-18T01:02:59.271741+00:00"}