{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:JQIHY7YHGRMBDLL4SLLA6B4WMT","short_pith_number":"pith:JQIHY7YH","canonical_record":{"source":{"id":"1703.09499","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-28T10:38:22Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"130178dc61c327fec58229df3ae994b57da3c439593784e38d8f13da342815af","abstract_canon_sha256":"27ef0e7a0693a2782e3d15373280233156e6ef95caf8ad7a0a091bd7057ebc51"},"schema_version":"1.0"},"canonical_sha256":"4c107c7f07345811ad7c92d60f079664f55fde8f844b2d48d5d2acaa9fb4edcc","source":{"kind":"arxiv","id":"1703.09499","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.09499","created_at":"2026-05-18T00:30:28Z"},{"alias_kind":"arxiv_version","alias_value":"1703.09499v2","created_at":"2026-05-18T00:30:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09499","created_at":"2026-05-18T00:30:28Z"},{"alias_kind":"pith_short_12","alias_value":"JQIHY7YHGRMB","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JQIHY7YHGRMBDLL4","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JQIHY7YH","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:JQIHY7YHGRMBDLL4SLLA6B4WMT","target":"record","payload":{"canonical_record":{"source":{"id":"1703.09499","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-28T10:38:22Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"130178dc61c327fec58229df3ae994b57da3c439593784e38d8f13da342815af","abstract_canon_sha256":"27ef0e7a0693a2782e3d15373280233156e6ef95caf8ad7a0a091bd7057ebc51"},"schema_version":"1.0"},"canonical_sha256":"4c107c7f07345811ad7c92d60f079664f55fde8f844b2d48d5d2acaa9fb4edcc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:28.389412Z","signature_b64":"9TDiNb77humocjFvRu2H5Yocc7L6Pdot+vtEAp58C9SoWevV3vu0jAQSb3LQd1Qr+hBvvjvE692jpG+6kRBWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c107c7f07345811ad7c92d60f079664f55fde8f844b2d48d5d2acaa9fb4edcc","last_reissued_at":"2026-05-18T00:30:28.388862Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:28.388862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.09499","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":"2026-05-18T00:30:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N43Xedf/nklvr0Knb9BBJyF0TZ6vlKU1Qq6A+r3kOc7WFJxDpwLHO7YMrb0zFy4iAkA6OiKdk88acSUJ2sniDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T23:13:46.339929Z"},"content_sha256":"3d78fdccb5468c7ce50f05bf8ce6e2443b7bf8472bd46dea45896cf86163dc26","schema_version":"1.0","event_id":"sha256:3d78fdccb5468c7ce50f05bf8ce6e2443b7bf8472bd46dea45896cf86163dc26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:JQIHY7YHGRMBDLL4SLLA6B4WMT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Locality preserving projection on SPD matrix Lie group: algorithm and analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"cs.CV","authors_text":"Ruqian Lu, Yangyang Li","submitted_at":"2017-03-28T10:38:22Z","abstract_excerpt":"Symmetric positive definite (SPD) matrices used as feature descriptors in image recognition are usually high dimensional. Traditional manifold learning is only applicable for reducing the dimension of high-dimensional vector-form data. For high-dimensional SPD matrices, directly using manifold learning algorithms to reduce the dimension of matrix-form data is impossible. The SPD matrix must first be transformed into a long vector, and then the dimension of this vector must be reduced. However, this approach breaks the spatial structure of the SPD matrix space. To overcome this limitation, we p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09499","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":"2026-05-18T00:30:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FwabHVKSAKjRazoSI5h9H4cTFTlXRN6gxxSUsrMczvf93gR9X3dXRGzTAdQEAnn653Qe8IkIoksre8bGoSnTAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T23:13:46.340647Z"},"content_sha256":"4e5736f874f7e4f4de222cc9f63b7506c2edfe2f686c6ab27609542fed852e18","schema_version":"1.0","event_id":"sha256:4e5736f874f7e4f4de222cc9f63b7506c2edfe2f686c6ab27609542fed852e18"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JQIHY7YHGRMBDLL4SLLA6B4WMT/bundle.json","state_url":"https://pith.science/pith/JQIHY7YHGRMBDLL4SLLA6B4WMT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JQIHY7YHGRMBDLL4SLLA6B4WMT/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":"2026-06-11T23:13:46Z","links":{"resolver":"https://pith.science/pith/JQIHY7YHGRMBDLL4SLLA6B4WMT","bundle":"https://pith.science/pith/JQIHY7YHGRMBDLL4SLLA6B4WMT/bundle.json","state":"https://pith.science/pith/JQIHY7YHGRMBDLL4SLLA6B4WMT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JQIHY7YHGRMBDLL4SLLA6B4WMT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:JQIHY7YHGRMBDLL4SLLA6B4WMT","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":"27ef0e7a0693a2782e3d15373280233156e6ef95caf8ad7a0a091bd7057ebc51","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-28T10:38:22Z","title_canon_sha256":"130178dc61c327fec58229df3ae994b57da3c439593784e38d8f13da342815af"},"schema_version":"1.0","source":{"id":"1703.09499","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.09499","created_at":"2026-05-18T00:30:28Z"},{"alias_kind":"arxiv_version","alias_value":"1703.09499v2","created_at":"2026-05-18T00:30:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.09499","created_at":"2026-05-18T00:30:28Z"},{"alias_kind":"pith_short_12","alias_value":"JQIHY7YHGRMB","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JQIHY7YHGRMBDLL4","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JQIHY7YH","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:4e5736f874f7e4f4de222cc9f63b7506c2edfe2f686c6ab27609542fed852e18","target":"graph","created_at":"2026-05-18T00:30:28Z","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":"Symmetric positive definite (SPD) matrices used as feature descriptors in image recognition are usually high dimensional. Traditional manifold learning is only applicable for reducing the dimension of high-dimensional vector-form data. For high-dimensional SPD matrices, directly using manifold learning algorithms to reduce the dimension of matrix-form data is impossible. The SPD matrix must first be transformed into a long vector, and then the dimension of this vector must be reduced. However, this approach breaks the spatial structure of the SPD matrix space. To overcome this limitation, we p","authors_text":"Ruqian Lu, Yangyang Li","cross_cats":["cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-28T10:38:22Z","title":"Locality preserving projection on SPD matrix Lie group: algorithm and analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.09499","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":"sha256:3d78fdccb5468c7ce50f05bf8ce6e2443b7bf8472bd46dea45896cf86163dc26","target":"record","created_at":"2026-05-18T00:30:28Z","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":"27ef0e7a0693a2782e3d15373280233156e6ef95caf8ad7a0a091bd7057ebc51","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-28T10:38:22Z","title_canon_sha256":"130178dc61c327fec58229df3ae994b57da3c439593784e38d8f13da342815af"},"schema_version":"1.0","source":{"id":"1703.09499","kind":"arxiv","version":2}},"canonical_sha256":"4c107c7f07345811ad7c92d60f079664f55fde8f844b2d48d5d2acaa9fb4edcc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4c107c7f07345811ad7c92d60f079664f55fde8f844b2d48d5d2acaa9fb4edcc","first_computed_at":"2026-05-18T00:30:28.388862Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:30:28.388862Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9TDiNb77humocjFvRu2H5Yocc7L6Pdot+vtEAp58C9SoWevV3vu0jAQSb3LQd1Qr+hBvvjvE692jpG+6kRBWDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:30:28.389412Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.09499","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d78fdccb5468c7ce50f05bf8ce6e2443b7bf8472bd46dea45896cf86163dc26","sha256:4e5736f874f7e4f4de222cc9f63b7506c2edfe2f686c6ab27609542fed852e18"],"state_sha256":"eebf78a46c855700642d001c8b776c917bb304b071f6450a1118587c5c9e14d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cSLRhTB1p8yl8UqKTRxJfxkY175TKYXwoD+ZzIhtBdOWJhoL4IdeuARh+KY5aG4pk1Kfv3yCsm8OG9wKw4A7AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T23:13:46.344753Z","bundle_sha256":"d2d5db4bd0b5e5e043da5f8a08caf0baf4a14450ec001dfefec973f1a05fddcf"}}