{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:TSPPSSRXVPLGGC53WQPDIX2ZWV","short_pith_number":"pith:TSPPSSRX","canonical_record":{"source":{"id":"1702.05698","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-19T04:08:18Z","cross_cats_sorted":["cs.CV","stat.AP","stat.CO","stat.ML"],"title_canon_sha256":"46b384e85c23c976164c064fb54ea86e8659b0b994819c370aa9345238f82dbb","abstract_canon_sha256":"e2ee0bc14c4fc2896893e3fc26ab79b205e86e13485c614ba13c91f40a2bc282"},"schema_version":"1.0"},"canonical_sha256":"9c9ef94a37abd6630bbbb41e345f59b55a7ea94b2f2d515320486a4474b7c39f","source":{"kind":"arxiv","id":"1702.05698","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.05698","created_at":"2026-05-18T00:48:14Z"},{"alias_kind":"arxiv_version","alias_value":"1702.05698v2","created_at":"2026-05-18T00:48:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.05698","created_at":"2026-05-18T00:48:14Z"},{"alias_kind":"pith_short_12","alias_value":"TSPPSSRXVPLG","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TSPPSSRXVPLGGC53","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TSPPSSRX","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:TSPPSSRXVPLGGC53WQPDIX2ZWV","target":"record","payload":{"canonical_record":{"source":{"id":"1702.05698","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-19T04:08:18Z","cross_cats_sorted":["cs.CV","stat.AP","stat.CO","stat.ML"],"title_canon_sha256":"46b384e85c23c976164c064fb54ea86e8659b0b994819c370aa9345238f82dbb","abstract_canon_sha256":"e2ee0bc14c4fc2896893e3fc26ab79b205e86e13485c614ba13c91f40a2bc282"},"schema_version":"1.0"},"canonical_sha256":"9c9ef94a37abd6630bbbb41e345f59b55a7ea94b2f2d515320486a4474b7c39f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:14.533519Z","signature_b64":"rvjUXb/eueL0QWiz2N5W6IRmZAlFjgUkJY+tgJMKmgWapuQhzCZZLrAy+cQZPnweCyykWfHZZhjYZZ/a5GzqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c9ef94a37abd6630bbbb41e345f59b55a7ea94b2f2d515320486a4474b7c39f","last_reissued_at":"2026-05-18T00:48:14.532885Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:14.532885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.05698","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:48:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GSPGrgVkocM5frQZWFwyRe6u3iCVuxOlZ3AwfyHnK5NkaO4XJKrqGrB8CpdI83MdNBWpe074ZFNbON1lsNbZCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T22:33:02.137922Z"},"content_sha256":"b5a626ea329ac55629524e47562fcf7891f3ee78d161df9417b48e576517e66c","schema_version":"1.0","event_id":"sha256:b5a626ea329ac55629524e47562fcf7891f3ee78d161df9417b48e576517e66c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:TSPPSSRXVPLGGC53WQPDIX2ZWV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online Robust Principal Component Analysis with Change Point Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.AP","stat.CO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arin Chaudhuri, Jorge Silva, Saba Emrani, Wei Xiao, Xiaolin Huang","submitted_at":"2017-02-19T04:08:18Z","abstract_excerpt":"Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this paper, we develop an efficient online robust principal component methods, namely online moving window robust principal component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can successfully track not only slowly changing subspace but also abruptly changed subspace. By embedding hypothesis testing into the algorithm, OMWRPCA can detect change points of the underlying subspaces. Extensive simulation studies d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.05698","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:48:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UL9xYJ9yVUYm3PMgymxkdaNF+Y9s6NQ30DbC8IeH/TwJgYrRDodI/lb2P+1kq+vQW9k1aJqZk/5XPg3b/28rAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T22:33:02.138348Z"},"content_sha256":"cb1f2c6a44856583d5f69d5338d2d609acd4a93cf7ec5323e6e911947cc01d3d","schema_version":"1.0","event_id":"sha256:cb1f2c6a44856583d5f69d5338d2d609acd4a93cf7ec5323e6e911947cc01d3d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TSPPSSRXVPLGGC53WQPDIX2ZWV/bundle.json","state_url":"https://pith.science/pith/TSPPSSRXVPLGGC53WQPDIX2ZWV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TSPPSSRXVPLGGC53WQPDIX2ZWV/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":"2026-06-20T22:33:02Z","links":{"resolver":"https://pith.science/pith/TSPPSSRXVPLGGC53WQPDIX2ZWV","bundle":"https://pith.science/pith/TSPPSSRXVPLGGC53WQPDIX2ZWV/bundle.json","state":"https://pith.science/pith/TSPPSSRXVPLGGC53WQPDIX2ZWV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TSPPSSRXVPLGGC53WQPDIX2ZWV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:TSPPSSRXVPLGGC53WQPDIX2ZWV","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":"e2ee0bc14c4fc2896893e3fc26ab79b205e86e13485c614ba13c91f40a2bc282","cross_cats_sorted":["cs.CV","stat.AP","stat.CO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-19T04:08:18Z","title_canon_sha256":"46b384e85c23c976164c064fb54ea86e8659b0b994819c370aa9345238f82dbb"},"schema_version":"1.0","source":{"id":"1702.05698","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.05698","created_at":"2026-05-18T00:48:14Z"},{"alias_kind":"arxiv_version","alias_value":"1702.05698v2","created_at":"2026-05-18T00:48:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.05698","created_at":"2026-05-18T00:48:14Z"},{"alias_kind":"pith_short_12","alias_value":"TSPPSSRXVPLG","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"TSPPSSRXVPLGGC53","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"TSPPSSRX","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:cb1f2c6a44856583d5f69d5338d2d609acd4a93cf7ec5323e6e911947cc01d3d","target":"graph","created_at":"2026-05-18T00:48:14Z","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":"Robust PCA methods are typically batch algorithms which requires loading all observations into memory before processing. This makes them inefficient to process big data. In this paper, we develop an efficient online robust principal component methods, namely online moving window robust principal component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can successfully track not only slowly changing subspace but also abruptly changed subspace. By embedding hypothesis testing into the algorithm, OMWRPCA can detect change points of the underlying subspaces. Extensive simulation studies d","authors_text":"Arin Chaudhuri, Jorge Silva, Saba Emrani, Wei Xiao, Xiaolin Huang","cross_cats":["cs.CV","stat.AP","stat.CO","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-19T04:08:18Z","title":"Online Robust Principal Component Analysis with Change Point Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.05698","kind":"arxiv","version":2},"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:b5a626ea329ac55629524e47562fcf7891f3ee78d161df9417b48e576517e66c","target":"record","created_at":"2026-05-18T00:48:14Z","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":"e2ee0bc14c4fc2896893e3fc26ab79b205e86e13485c614ba13c91f40a2bc282","cross_cats_sorted":["cs.CV","stat.AP","stat.CO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-02-19T04:08:18Z","title_canon_sha256":"46b384e85c23c976164c064fb54ea86e8659b0b994819c370aa9345238f82dbb"},"schema_version":"1.0","source":{"id":"1702.05698","kind":"arxiv","version":2}},"canonical_sha256":"9c9ef94a37abd6630bbbb41e345f59b55a7ea94b2f2d515320486a4474b7c39f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9c9ef94a37abd6630bbbb41e345f59b55a7ea94b2f2d515320486a4474b7c39f","first_computed_at":"2026-05-18T00:48:14.532885Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:48:14.532885Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rvjUXb/eueL0QWiz2N5W6IRmZAlFjgUkJY+tgJMKmgWapuQhzCZZLrAy+cQZPnweCyykWfHZZhjYZZ/a5GzqCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:48:14.533519Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.05698","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b5a626ea329ac55629524e47562fcf7891f3ee78d161df9417b48e576517e66c","sha256:cb1f2c6a44856583d5f69d5338d2d609acd4a93cf7ec5323e6e911947cc01d3d"],"state_sha256":"5e515562151da87fc514968fe34bfdcd151a75795593919e587ebf5dc4d45b5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"udjk/ngWEbp67sPOo0Trj/Bxg1Pax/QGwTOmXSgMB9EN1tC8acMCw8uRbNjG1ioqQxQ+hcjQj1XVeIZ9Kx4QDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T22:33:02.140373Z","bundle_sha256":"6eea2d046c821530a8f926f9eca1a38d45fc17931440b11c021bdb9629e0a351"}}