{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:ZBAGDD4L3AZW35MXQXNY7BWATZ","short_pith_number":"pith:ZBAGDD4L","canonical_record":{"source":{"id":"1402.3514","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-02-14T16:13:21Z","cross_cats_sorted":["math.ST","stat.AP","stat.CO","stat.TH"],"title_canon_sha256":"26e54690ddee219e78ba09fcf63e40714482a6ca959dc6084a98274125ec3e7a","abstract_canon_sha256":"e894e9c36b124d2e8d21971c4b4bac51bd8191142887fbd0bf30ece0072707f1"},"schema_version":"1.0"},"canonical_sha256":"c840618f8bd8336df59785db8f86c09e6e29073a65610ea0d94d659bdffc0f46","source":{"kind":"arxiv","id":"1402.3514","version":7},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.3514","created_at":"2026-05-18T01:32:10Z"},{"alias_kind":"arxiv_version","alias_value":"1402.3514v7","created_at":"2026-05-18T01:32:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.3514","created_at":"2026-05-18T01:32:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZBAGDD4L3AZW","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZBAGDD4L3AZW35MX","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZBAGDD4L","created_at":"2026-05-18T12:28:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:ZBAGDD4L3AZW35MXQXNY7BWATZ","target":"record","payload":{"canonical_record":{"source":{"id":"1402.3514","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-02-14T16:13:21Z","cross_cats_sorted":["math.ST","stat.AP","stat.CO","stat.TH"],"title_canon_sha256":"26e54690ddee219e78ba09fcf63e40714482a6ca959dc6084a98274125ec3e7a","abstract_canon_sha256":"e894e9c36b124d2e8d21971c4b4bac51bd8191142887fbd0bf30ece0072707f1"},"schema_version":"1.0"},"canonical_sha256":"c840618f8bd8336df59785db8f86c09e6e29073a65610ea0d94d659bdffc0f46","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:32:10.939463Z","signature_b64":"c5WpECK1ERY0pC0bKrQnEjP7iaOetDA2tLPsDhnZESu8+38QhlBClvbW0RccYR+1BsmlMYooZotrYAQE69JYCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c840618f8bd8336df59785db8f86c09e6e29073a65610ea0d94d659bdffc0f46","last_reissued_at":"2026-05-18T01:32:10.938773Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:32:10.938773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1402.3514","source_version":7,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:32:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"544N3JSjMSF5PJ4LbstvLYqM4a6pvYjuTv2oz+m0Ql6I0/zMVA/P6fP2CTtfUgtfJywZ4IC+3eKu3xM1T2+hDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T22:28:52.088882Z"},"content_sha256":"d8c1819cf866596e3887c31cd8a50c765cca8f51e2d54b9b8f87b4baaf154835","schema_version":"1.0","event_id":"sha256:d8c1819cf866596e3887c31cd8a50c765cca8f51e2d54b9b8f87b4baaf154835"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:ZBAGDD4L3AZW35MXQXNY7BWATZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust PCA with FastHCS","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.AP","stat.CO","stat.TH"],"primary_cat":"stat.ME","authors_text":"E. Schmitt, K. Vakili","submitted_at":"2014-02-14T16:13:21Z","abstract_excerpt":"Principal component analysis (PCA) is widely used to analyze high-dimensional data, but it is very sensitive to outliers. Robust PCA methods seek fits that are unaffected by the outliers and can therefore be trusted to reveal them. FastHCS (High-dimensional Congruent Subsets) is a robust PCA algorithm suitable for high-dimensional applications, including cases where the number of variables exceeds the number of observations. After detailing the FastHCS algorithm, we carry out an extensive simulation study and three real data applications, the results of which show that FastHCS is systematicall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.3514","kind":"arxiv","version":7},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:32:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pDAUBY8C/oF157Vj6B76HVoF9hKayVZj4b8l/5Ld6fjSJOcPKjK4roaO0CHksks+egKF8qYYDXbruKbnZZozBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T22:28:52.089227Z"},"content_sha256":"4242c37b459e8ead15972fefdd162119904b0ce12004498e4d89fc96d0685003","schema_version":"1.0","event_id":"sha256:4242c37b459e8ead15972fefdd162119904b0ce12004498e4d89fc96d0685003"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZBAGDD4L3AZW35MXQXNY7BWATZ/bundle.json","state_url":"https://pith.science/pith/ZBAGDD4L3AZW35MXQXNY7BWATZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZBAGDD4L3AZW35MXQXNY7BWATZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-21T22:28:52Z","links":{"resolver":"https://pith.science/pith/ZBAGDD4L3AZW35MXQXNY7BWATZ","bundle":"https://pith.science/pith/ZBAGDD4L3AZW35MXQXNY7BWATZ/bundle.json","state":"https://pith.science/pith/ZBAGDD4L3AZW35MXQXNY7BWATZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZBAGDD4L3AZW35MXQXNY7BWATZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:ZBAGDD4L3AZW35MXQXNY7BWATZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e894e9c36b124d2e8d21971c4b4bac51bd8191142887fbd0bf30ece0072707f1","cross_cats_sorted":["math.ST","stat.AP","stat.CO","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-02-14T16:13:21Z","title_canon_sha256":"26e54690ddee219e78ba09fcf63e40714482a6ca959dc6084a98274125ec3e7a"},"schema_version":"1.0","source":{"id":"1402.3514","kind":"arxiv","version":7}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.3514","created_at":"2026-05-18T01:32:10Z"},{"alias_kind":"arxiv_version","alias_value":"1402.3514v7","created_at":"2026-05-18T01:32:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.3514","created_at":"2026-05-18T01:32:10Z"},{"alias_kind":"pith_short_12","alias_value":"ZBAGDD4L3AZW","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZBAGDD4L3AZW35MX","created_at":"2026-05-18T12:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZBAGDD4L","created_at":"2026-05-18T12:28:59Z"}],"graph_snapshots":[{"event_id":"sha256:4242c37b459e8ead15972fefdd162119904b0ce12004498e4d89fc96d0685003","target":"graph","created_at":"2026-05-18T01:32:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Principal component analysis (PCA) is widely used to analyze high-dimensional data, but it is very sensitive to outliers. Robust PCA methods seek fits that are unaffected by the outliers and can therefore be trusted to reveal them. FastHCS (High-dimensional Congruent Subsets) is a robust PCA algorithm suitable for high-dimensional applications, including cases where the number of variables exceeds the number of observations. After detailing the FastHCS algorithm, we carry out an extensive simulation study and three real data applications, the results of which show that FastHCS is systematicall","authors_text":"E. Schmitt, K. Vakili","cross_cats":["math.ST","stat.AP","stat.CO","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-02-14T16:13:21Z","title":"Robust PCA with FastHCS"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.3514","kind":"arxiv","version":7},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d8c1819cf866596e3887c31cd8a50c765cca8f51e2d54b9b8f87b4baaf154835","target":"record","created_at":"2026-05-18T01:32:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e894e9c36b124d2e8d21971c4b4bac51bd8191142887fbd0bf30ece0072707f1","cross_cats_sorted":["math.ST","stat.AP","stat.CO","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-02-14T16:13:21Z","title_canon_sha256":"26e54690ddee219e78ba09fcf63e40714482a6ca959dc6084a98274125ec3e7a"},"schema_version":"1.0","source":{"id":"1402.3514","kind":"arxiv","version":7}},"canonical_sha256":"c840618f8bd8336df59785db8f86c09e6e29073a65610ea0d94d659bdffc0f46","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c840618f8bd8336df59785db8f86c09e6e29073a65610ea0d94d659bdffc0f46","first_computed_at":"2026-05-18T01:32:10.938773Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:32:10.938773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"c5WpECK1ERY0pC0bKrQnEjP7iaOetDA2tLPsDhnZESu8+38QhlBClvbW0RccYR+1BsmlMYooZotrYAQE69JYCA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:32:10.939463Z","signed_message":"canonical_sha256_bytes"},"source_id":"1402.3514","source_kind":"arxiv","source_version":7}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8c1819cf866596e3887c31cd8a50c765cca8f51e2d54b9b8f87b4baaf154835","sha256:4242c37b459e8ead15972fefdd162119904b0ce12004498e4d89fc96d0685003"],"state_sha256":"d173dfd4aef8b49b0644c86ab1598eeb69850ee152e0e49a3dc5a3224cbba061"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"opHbUbfVmxqCoLSO+sgqmaljNrOh0KoHt0GwmXy/bTnW/jcx3dNu9wHxCrebg9YYrho7CyDtoeQJNIjshLdJAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T22:28:52.091175Z","bundle_sha256":"4aac1783ca9490249d642703c0cbade3913338c9602e7789376a94b54b652f7b"}}