{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LUSFVIHXLWYOSYWK4GB5EQ47YT","short_pith_number":"pith:LUSFVIHX","canonical_record":{"source":{"id":"2507.13887","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-07-18T13:05:42Z","cross_cats_sorted":["cs.LG","math.DG","math.MG","math.ST","stat.TH"],"title_canon_sha256":"1604cfa2fbf4fcb945323c52154404ad6732493eae10a3f489ed3e6b79c9d872","abstract_canon_sha256":"eeebf0e2cbab1d572c1632b96cd636fbb60052f63f90249a18e37bc5168ad920"},"schema_version":"1.0"},"canonical_sha256":"5d245aa0f75db0e962cae183d2439fc4cf1c38daf2d91060f46004bfb667310e","source":{"kind":"arxiv","id":"2507.13887","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.13887","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"arxiv_version","alias_value":"2507.13887v1","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13887","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"pith_short_12","alias_value":"LUSFVIHXLWYO","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"pith_short_16","alias_value":"LUSFVIHXLWYOSYWK","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"pith_short_8","alias_value":"LUSFVIHX","created_at":"2026-07-05T11:39:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LUSFVIHXLWYOSYWK4GB5EQ47YT","target":"record","payload":{"canonical_record":{"source":{"id":"2507.13887","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-07-18T13:05:42Z","cross_cats_sorted":["cs.LG","math.DG","math.MG","math.ST","stat.TH"],"title_canon_sha256":"1604cfa2fbf4fcb945323c52154404ad6732493eae10a3f489ed3e6b79c9d872","abstract_canon_sha256":"eeebf0e2cbab1d572c1632b96cd636fbb60052f63f90249a18e37bc5168ad920"},"schema_version":"1.0"},"canonical_sha256":"5d245aa0f75db0e962cae183d2439fc4cf1c38daf2d91060f46004bfb667310e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:25.042333Z","signature_b64":"XYV2+AtXOck90mfGuy5hCJS4kWTFgIUeQLOXL/1/jHkeZJxFiCYkfk5ybmJeNqGTR31Kx0hnDTbp/QJv77J+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d245aa0f75db0e962cae183d2439fc4cf1c38daf2d91060f46004bfb667310e","last_reissued_at":"2026-07-05T11:39:25.041849Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:25.041849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.13887","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":"2026-07-05T11:39:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ptyZLGUAU1vGSQw6vjoB4HWDTyZhhD9UjeAlcTIUnfPLfEogOicp1DZOD8W2x+KHp0o/9Q59MPbgFF3P+CugAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:22:31.122375Z"},"content_sha256":"53834e6209560e811a240bd2630488a6c7f154ec2237f7f148493d6e4eb9a99e","schema_version":"1.0","event_id":"sha256:53834e6209560e811a240bd2630488a6c7f154ec2237f7f148493d6e4eb9a99e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LUSFVIHXLWYOSYWK4GB5EQ47YT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey of Dimension Estimation Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.DG","math.MG","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Jakub Malinowski, James A. D. Binnie, John Harvey, Ka Man Yim, Pawe{\\l} D{\\l}otko","submitted_at":"2025-07-18T13:05:42Z","abstract_excerpt":"It is a standard assumption that datasets in high dimension have an internal structure which means that they in fact lie on, or near, subsets of a lower dimension. In many instances it is important to understand the real dimension of the data, hence the complexity of the dataset at hand. A great variety of dimension estimators have been developed to find the intrinsic dimension of the data but there is little guidance on how to reliably use these estimators.\n  This survey reviews a wide range of dimension estimation methods, categorising them by the geometric information they exploit: tangenti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13887","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.13887/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T11:39:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3C+6JBGktDOP9tPo84lvrXXzckI5FrbUl1k1y7QADhqZhOtXZ+vooYPaTBUXXeLFkDdDVmBQiEMfKmsEwD37Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:22:31.122941Z"},"content_sha256":"649c88a4f3c29adc8c25eac8e3d52cbcbe480ca5f6f3d2ca64aa8c212eb8b7f3","schema_version":"1.0","event_id":"sha256:649c88a4f3c29adc8c25eac8e3d52cbcbe480ca5f6f3d2ca64aa8c212eb8b7f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LUSFVIHXLWYOSYWK4GB5EQ47YT/bundle.json","state_url":"https://pith.science/pith/LUSFVIHXLWYOSYWK4GB5EQ47YT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LUSFVIHXLWYOSYWK4GB5EQ47YT/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":"2026-08-18T21:22:31Z","links":{"resolver":"https://pith.science/pith/LUSFVIHXLWYOSYWK4GB5EQ47YT","bundle":"https://pith.science/pith/LUSFVIHXLWYOSYWK4GB5EQ47YT/bundle.json","state":"https://pith.science/pith/LUSFVIHXLWYOSYWK4GB5EQ47YT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LUSFVIHXLWYOSYWK4GB5EQ47YT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LUSFVIHXLWYOSYWK4GB5EQ47YT","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":"eeebf0e2cbab1d572c1632b96cd636fbb60052f63f90249a18e37bc5168ad920","cross_cats_sorted":["cs.LG","math.DG","math.MG","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-07-18T13:05:42Z","title_canon_sha256":"1604cfa2fbf4fcb945323c52154404ad6732493eae10a3f489ed3e6b79c9d872"},"schema_version":"1.0","source":{"id":"2507.13887","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.13887","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"arxiv_version","alias_value":"2507.13887v1","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13887","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"pith_short_12","alias_value":"LUSFVIHXLWYO","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"pith_short_16","alias_value":"LUSFVIHXLWYOSYWK","created_at":"2026-07-05T11:39:25Z"},{"alias_kind":"pith_short_8","alias_value":"LUSFVIHX","created_at":"2026-07-05T11:39:25Z"}],"graph_snapshots":[{"event_id":"sha256:649c88a4f3c29adc8c25eac8e3d52cbcbe480ca5f6f3d2ca64aa8c212eb8b7f3","target":"graph","created_at":"2026-07-05T11:39:25Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2507.13887/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"It is a standard assumption that datasets in high dimension have an internal structure which means that they in fact lie on, or near, subsets of a lower dimension. In many instances it is important to understand the real dimension of the data, hence the complexity of the dataset at hand. A great variety of dimension estimators have been developed to find the intrinsic dimension of the data but there is little guidance on how to reliably use these estimators.\n  This survey reviews a wide range of dimension estimation methods, categorising them by the geometric information they exploit: tangenti","authors_text":"Jakub Malinowski, James A. D. Binnie, John Harvey, Ka Man Yim, Pawe{\\l} D{\\l}otko","cross_cats":["cs.LG","math.DG","math.MG","math.ST","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-07-18T13:05:42Z","title":"A Survey of Dimension Estimation Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13887","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":"sha256:53834e6209560e811a240bd2630488a6c7f154ec2237f7f148493d6e4eb9a99e","target":"record","created_at":"2026-07-05T11:39:25Z","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":"eeebf0e2cbab1d572c1632b96cd636fbb60052f63f90249a18e37bc5168ad920","cross_cats_sorted":["cs.LG","math.DG","math.MG","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-07-18T13:05:42Z","title_canon_sha256":"1604cfa2fbf4fcb945323c52154404ad6732493eae10a3f489ed3e6b79c9d872"},"schema_version":"1.0","source":{"id":"2507.13887","kind":"arxiv","version":1}},"canonical_sha256":"5d245aa0f75db0e962cae183d2439fc4cf1c38daf2d91060f46004bfb667310e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d245aa0f75db0e962cae183d2439fc4cf1c38daf2d91060f46004bfb667310e","first_computed_at":"2026-07-05T11:39:25.041849Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:39:25.041849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XYV2+AtXOck90mfGuy5hCJS4kWTFgIUeQLOXL/1/jHkeZJxFiCYkfk5ybmJeNqGTR31Kx0hnDTbp/QJv77J+CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:39:25.042333Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.13887","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53834e6209560e811a240bd2630488a6c7f154ec2237f7f148493d6e4eb9a99e","sha256:649c88a4f3c29adc8c25eac8e3d52cbcbe480ca5f6f3d2ca64aa8c212eb8b7f3"],"state_sha256":"60360d89f0f6d1cee36a51c03edaa2d3fa41ff70a794eb2bbdc582d745de98a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nQGdiO4nWuLXAjz1NXTh0YCECRPjSpPVf8oFqm8sSoV3urDgy4yEjxxkN6HkFJk42la+tDztgdba/ms+8n+vAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T21:22:31.127373Z","bundle_sha256":"352bc6de1d8ba3f38a2f56e4d370975665472986cf1f2e20f8b7cdbd458ded7c"}}