{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:ZLB7CKMGPNOFIQBIUK6MNXQJFV","short_pith_number":"pith:ZLB7CKMG","canonical_record":{"source":{"id":"1601.07985","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-01-29T06:29:36Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"812f0f822f1553d14593a05bc586fe0785a54cf5e8888f62f32d21573dd7766a","abstract_canon_sha256":"576cf7f52e32ea46b109b3de0924c4b09724df80dbfb30ea768dd04106e404b9"},"schema_version":"1.0"},"canonical_sha256":"cac3f129867b5c544028a2bcc6de092d4caff122834ae9b6d9d976346303bd78","source":{"kind":"arxiv","id":"1601.07985","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.07985","created_at":"2026-05-18T01:21:41Z"},{"alias_kind":"arxiv_version","alias_value":"1601.07985v1","created_at":"2026-05-18T01:21:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.07985","created_at":"2026-05-18T01:21:41Z"},{"alias_kind":"pith_short_12","alias_value":"ZLB7CKMGPNOF","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"ZLB7CKMGPNOFIQBI","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"ZLB7CKMG","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:ZLB7CKMGPNOFIQBIUK6MNXQJFV","target":"record","payload":{"canonical_record":{"source":{"id":"1601.07985","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-01-29T06:29:36Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"812f0f822f1553d14593a05bc586fe0785a54cf5e8888f62f32d21573dd7766a","abstract_canon_sha256":"576cf7f52e32ea46b109b3de0924c4b09724df80dbfb30ea768dd04106e404b9"},"schema_version":"1.0"},"canonical_sha256":"cac3f129867b5c544028a2bcc6de092d4caff122834ae9b6d9d976346303bd78","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:21:41.324102Z","signature_b64":"MIDzd1JYcKUYROWYhsoLLsS4y35dugY4sF4QDv5RceLhMiTPPDFv5EgSw1bips0AChCj/PuysWsDX45725NHDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cac3f129867b5c544028a2bcc6de092d4caff122834ae9b6d9d976346303bd78","last_reissued_at":"2026-05-18T01:21:41.323508Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:21:41.323508Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1601.07985","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:21:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4FkK9Qo9VWdJQFg7OwekjsuAvzdMdbUfE/LxwQS42bxyShHJdCvAP2CIK0LGMIV0thseTURWMFsd7bf4jX9vCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:07:19.284689Z"},"content_sha256":"64dfc1d1ba1856f5d456fa1a040c6483f12b776f9fe4b9e9516f6c288b4c2188","schema_version":"1.0","event_id":"sha256:64dfc1d1ba1856f5d456fa1a040c6483f12b776f9fe4b9e9516f6c288b4c2188"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:ZLB7CKMGPNOFIQBIUK6MNXQJFV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Brian Lois, Jinchun Zhan, Namrata Vaswani","submitted_at":"2016-01-29T06:29:36Z","abstract_excerpt":"In this work, we study the online robust principal components' analysis (RPCA) problem. In recent work, RPCA has been defined as a problem of separating a low-rank matrix (true data), $L$, and a sparse matrix (outliers), $S$, from their sum, $M:=L + S$. A more general version of this problem is to recover $L$ and $S$ from $M:=L + S + W$ where $W$ is the matrix of unstructured small noise/corruptions. An important application where this problem occurs is in video analytics in trying to separate sparse foregrounds (e.g., moving objects) from slowly changing backgrounds. While there has been a la"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.07985","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:21:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pDLVm3SxTwG5mUxHL9a3fnzCMqPbJF1YbXdnEyQY9LeEIO2obZ+YWDQM3gGlRUTkvMhALs2BIfRUHm7FG3nYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T15:07:19.285216Z"},"content_sha256":"c5bb74a0a15835f9f16c8f72a9409ece9d577736d0258cc7dd046e3a278160d5","schema_version":"1.0","event_id":"sha256:c5bb74a0a15835f9f16c8f72a9409ece9d577736d0258cc7dd046e3a278160d5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZLB7CKMGPNOFIQBIUK6MNXQJFV/bundle.json","state_url":"https://pith.science/pith/ZLB7CKMGPNOFIQBIUK6MNXQJFV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZLB7CKMGPNOFIQBIUK6MNXQJFV/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":"2026-05-30T15:07:19Z","links":{"resolver":"https://pith.science/pith/ZLB7CKMGPNOFIQBIUK6MNXQJFV","bundle":"https://pith.science/pith/ZLB7CKMGPNOFIQBIUK6MNXQJFV/bundle.json","state":"https://pith.science/pith/ZLB7CKMGPNOFIQBIUK6MNXQJFV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZLB7CKMGPNOFIQBIUK6MNXQJFV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:ZLB7CKMGPNOFIQBIUK6MNXQJFV","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":"576cf7f52e32ea46b109b3de0924c4b09724df80dbfb30ea768dd04106e404b9","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-01-29T06:29:36Z","title_canon_sha256":"812f0f822f1553d14593a05bc586fe0785a54cf5e8888f62f32d21573dd7766a"},"schema_version":"1.0","source":{"id":"1601.07985","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1601.07985","created_at":"2026-05-18T01:21:41Z"},{"alias_kind":"arxiv_version","alias_value":"1601.07985v1","created_at":"2026-05-18T01:21:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.07985","created_at":"2026-05-18T01:21:41Z"},{"alias_kind":"pith_short_12","alias_value":"ZLB7CKMGPNOF","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"ZLB7CKMGPNOFIQBI","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"ZLB7CKMG","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:c5bb74a0a15835f9f16c8f72a9409ece9d577736d0258cc7dd046e3a278160d5","target":"graph","created_at":"2026-05-18T01:21:41Z","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":"In this work, we study the online robust principal components' analysis (RPCA) problem. In recent work, RPCA has been defined as a problem of separating a low-rank matrix (true data), $L$, and a sparse matrix (outliers), $S$, from their sum, $M:=L + S$. A more general version of this problem is to recover $L$ and $S$ from $M:=L + S + W$ where $W$ is the matrix of unstructured small noise/corruptions. An important application where this problem occurs is in video analytics in trying to separate sparse foregrounds (e.g., moving objects) from slowly changing backgrounds. While there has been a la","authors_text":"Brian Lois, Jinchun Zhan, Namrata Vaswani","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-01-29T06:29:36Z","title":"Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.07985","kind":"arxiv","version":1},"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":"sha256:64dfc1d1ba1856f5d456fa1a040c6483f12b776f9fe4b9e9516f6c288b4c2188","target":"record","created_at":"2026-05-18T01:21:41Z","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":"576cf7f52e32ea46b109b3de0924c4b09724df80dbfb30ea768dd04106e404b9","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-01-29T06:29:36Z","title_canon_sha256":"812f0f822f1553d14593a05bc586fe0785a54cf5e8888f62f32d21573dd7766a"},"schema_version":"1.0","source":{"id":"1601.07985","kind":"arxiv","version":1}},"canonical_sha256":"cac3f129867b5c544028a2bcc6de092d4caff122834ae9b6d9d976346303bd78","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cac3f129867b5c544028a2bcc6de092d4caff122834ae9b6d9d976346303bd78","first_computed_at":"2026-05-18T01:21:41.323508Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:21:41.323508Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MIDzd1JYcKUYROWYhsoLLsS4y35dugY4sF4QDv5RceLhMiTPPDFv5EgSw1bips0AChCj/PuysWsDX45725NHDw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:21:41.324102Z","signed_message":"canonical_sha256_bytes"},"source_id":"1601.07985","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:64dfc1d1ba1856f5d456fa1a040c6483f12b776f9fe4b9e9516f6c288b4c2188","sha256:c5bb74a0a15835f9f16c8f72a9409ece9d577736d0258cc7dd046e3a278160d5"],"state_sha256":"9d425810f729944ff6307335e10bb8668acabad94a2fb7f28e2f94cf8c4b805a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BmVrK8Ac+eNHZPAHtEsvlTpo7cRpjNLR7jNp6AIYe5K+Smv0s+twWNPB2/cMPqJ0Js2UKTONz+Rjpqj5E1OhDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T15:07:19.288123Z","bundle_sha256":"a1ad803507cee1111edb357b91f773c216c2170489a162a8a1edf56d4e9fcddd"}}