{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WMOY3VINZG5UORS6W5VVFDM7EK","short_pith_number":"pith:WMOY3VIN","canonical_record":{"source":{"id":"1804.03065","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-09T15:47:36Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"67385e13d3db46e2aaabebca030283e82889028a00c76849c101c6aa754491da","abstract_canon_sha256":"4a6e252de35cb6c1bc660171517ca4c937effd79b71d41636cf7ee30f08df661"},"schema_version":"1.0"},"canonical_sha256":"b31d8dd50dc9bb47465eb76b528d9f2291bfe945dc54348b2c286f8ead48396b","source":{"kind":"arxiv","id":"1804.03065","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.03065","created_at":"2026-05-17T23:59:52Z"},{"alias_kind":"arxiv_version","alias_value":"1804.03065v2","created_at":"2026-05-17T23:59:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.03065","created_at":"2026-05-17T23:59:52Z"},{"alias_kind":"pith_short_12","alias_value":"WMOY3VINZG5U","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WMOY3VINZG5UORS6","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WMOY3VIN","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WMOY3VINZG5UORS6W5VVFDM7EK","target":"record","payload":{"canonical_record":{"source":{"id":"1804.03065","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-09T15:47:36Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"67385e13d3db46e2aaabebca030283e82889028a00c76849c101c6aa754491da","abstract_canon_sha256":"4a6e252de35cb6c1bc660171517ca4c937effd79b71d41636cf7ee30f08df661"},"schema_version":"1.0"},"canonical_sha256":"b31d8dd50dc9bb47465eb76b528d9f2291bfe945dc54348b2c286f8ead48396b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:52.993350Z","signature_b64":"36YPigt5Xik6W5LqwPl0afqsvbn95hlcu4ZYVKMXJsQXpQDXhxHxmaJul6+CyWqbef2Hc0C+feFIU/BoQXlECQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b31d8dd50dc9bb47465eb76b528d9f2291bfe945dc54348b2c286f8ead48396b","last_reissued_at":"2026-05-17T23:59:52.992883Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:52.992883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.03065","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":"2026-05-17T23:59:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TFON8JlqdllCljlEXiClG9l8Odn+sydIBBG2bazNPapy3kMhH6zy60eQRsyGasGu5u6eEOFRmwiMGvg4RvrLBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T16:52:51.913043Z"},"content_sha256":"b67b50fc3287bb5d5141401e9d789699f400cf147b9ba3db041d1cfe60cc317f","schema_version":"1.0","event_id":"sha256:b67b50fc3287bb5d5141401e9d789699f400cf147b9ba3db041d1cfe60cc317f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WMOY3VINZG5UORS6W5VVFDM7EK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Anomaly Detection via Matrix Sketching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Parikshit Gopalan, Udi Wieder, Vatsal Sharan","submitted_at":"2018-04-09T15:47:36Z","abstract_excerpt":"We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms that use space that is linear or sublinear in the dimension. We prove general results showing that \\emph{any} sketch of a matrix that satisfies a certain operator norm guarantee can be used to approximate these scores. We instantiate these results with powerful matrix sketching techniques"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.03065","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":"2026-05-17T23:59:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"46SpU2fVUpoPFpKEn3+WrAObuBhmMdIk+rT83mXJZmb1q8gEl+KmNp5RleuGabjiXEJECqI+mOKP+A5mDzlABA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T16:52:51.913405Z"},"content_sha256":"27130d3839fbff1e38783f3bc7917b8a99b10dd7d00f3a1a170bd397457164d6","schema_version":"1.0","event_id":"sha256:27130d3839fbff1e38783f3bc7917b8a99b10dd7d00f3a1a170bd397457164d6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WMOY3VINZG5UORS6W5VVFDM7EK/bundle.json","state_url":"https://pith.science/pith/WMOY3VINZG5UORS6W5VVFDM7EK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WMOY3VINZG5UORS6W5VVFDM7EK/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":"2026-06-28T16:52:51Z","links":{"resolver":"https://pith.science/pith/WMOY3VINZG5UORS6W5VVFDM7EK","bundle":"https://pith.science/pith/WMOY3VINZG5UORS6W5VVFDM7EK/bundle.json","state":"https://pith.science/pith/WMOY3VINZG5UORS6W5VVFDM7EK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WMOY3VINZG5UORS6W5VVFDM7EK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WMOY3VINZG5UORS6W5VVFDM7EK","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":"4a6e252de35cb6c1bc660171517ca4c937effd79b71d41636cf7ee30f08df661","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-09T15:47:36Z","title_canon_sha256":"67385e13d3db46e2aaabebca030283e82889028a00c76849c101c6aa754491da"},"schema_version":"1.0","source":{"id":"1804.03065","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.03065","created_at":"2026-05-17T23:59:52Z"},{"alias_kind":"arxiv_version","alias_value":"1804.03065v2","created_at":"2026-05-17T23:59:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.03065","created_at":"2026-05-17T23:59:52Z"},{"alias_kind":"pith_short_12","alias_value":"WMOY3VINZG5U","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WMOY3VINZG5UORS6","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WMOY3VIN","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:27130d3839fbff1e38783f3bc7917b8a99b10dd7d00f3a1a170bd397457164d6","target":"graph","created_at":"2026-05-17T23:59:52Z","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":"We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms that use space that is linear or sublinear in the dimension. We prove general results showing that \\emph{any} sketch of a matrix that satisfies a certain operator norm guarantee can be used to approximate these scores. We instantiate these results with powerful matrix sketching techniques","authors_text":"Parikshit Gopalan, Udi Wieder, Vatsal Sharan","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-09T15:47:36Z","title":"Efficient Anomaly Detection via Matrix Sketching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.03065","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:b67b50fc3287bb5d5141401e9d789699f400cf147b9ba3db041d1cfe60cc317f","target":"record","created_at":"2026-05-17T23:59:52Z","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":"4a6e252de35cb6c1bc660171517ca4c937effd79b71d41636cf7ee30f08df661","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-09T15:47:36Z","title_canon_sha256":"67385e13d3db46e2aaabebca030283e82889028a00c76849c101c6aa754491da"},"schema_version":"1.0","source":{"id":"1804.03065","kind":"arxiv","version":2}},"canonical_sha256":"b31d8dd50dc9bb47465eb76b528d9f2291bfe945dc54348b2c286f8ead48396b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b31d8dd50dc9bb47465eb76b528d9f2291bfe945dc54348b2c286f8ead48396b","first_computed_at":"2026-05-17T23:59:52.992883Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:52.992883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"36YPigt5Xik6W5LqwPl0afqsvbn95hlcu4ZYVKMXJsQXpQDXhxHxmaJul6+CyWqbef2Hc0C+feFIU/BoQXlECQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:52.993350Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.03065","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b67b50fc3287bb5d5141401e9d789699f400cf147b9ba3db041d1cfe60cc317f","sha256:27130d3839fbff1e38783f3bc7917b8a99b10dd7d00f3a1a170bd397457164d6"],"state_sha256":"9a96a50f38b15b25f98092e9abfc92fc4700bda5d83bfada7bc74bf68a5be0a7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OZRwgkQvy7hFan6cFyiWFP/VBhdYjBp4HsgWiULZZ25US47yjPdzGDu9E8m6m7pqOw0+nz7HWk9xQlTYb9ItBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T16:52:51.917824Z","bundle_sha256":"973d77a3726eee3147856318c098ed9d5573f5650878e1f3d2c5f913cf83b0b4"}}