{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:HWEMPUW6ACO3GDPPVGNN73V2Z3","short_pith_number":"pith:HWEMPUW6","canonical_record":{"source":{"id":"1409.7313","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-09-25T16:14:18Z","cross_cats_sorted":[],"title_canon_sha256":"2aaca1d1b16ff693b551988c614f24e7371c47cd0511858ac890562e67c4349a","abstract_canon_sha256":"65a65854848e8896148c04eed2e3cc4ae936e443ee3e3489ae9d7950740ed28c"},"schema_version":"1.0"},"canonical_sha256":"3d88c7d2de009db30defa99adfeebacef82b04af51c791b733bf15960ee72a8b","source":{"kind":"arxiv","id":"1409.7313","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.7313","created_at":"2026-05-18T02:41:58Z"},{"alias_kind":"arxiv_version","alias_value":"1409.7313v1","created_at":"2026-05-18T02:41:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.7313","created_at":"2026-05-18T02:41:58Z"},{"alias_kind":"pith_short_12","alias_value":"HWEMPUW6ACO3","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"HWEMPUW6ACO3GDPP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"HWEMPUW6","created_at":"2026-05-18T12:28:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:HWEMPUW6ACO3GDPPVGNN73V2Z3","target":"record","payload":{"canonical_record":{"source":{"id":"1409.7313","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-09-25T16:14:18Z","cross_cats_sorted":[],"title_canon_sha256":"2aaca1d1b16ff693b551988c614f24e7371c47cd0511858ac890562e67c4349a","abstract_canon_sha256":"65a65854848e8896148c04eed2e3cc4ae936e443ee3e3489ae9d7950740ed28c"},"schema_version":"1.0"},"canonical_sha256":"3d88c7d2de009db30defa99adfeebacef82b04af51c791b733bf15960ee72a8b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:41:58.691810Z","signature_b64":"RlZjdbuRl8S/ac5qgtbem87OXuILYQZwnHE6l8PNhlyUK+/RPZAhdoock8NwD81gXryWSSPnZ4cCmA/WNvvSDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d88c7d2de009db30defa99adfeebacef82b04af51c791b733bf15960ee72a8b","last_reissued_at":"2026-05-18T02:41:58.691394Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:41:58.691394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.7313","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":"2026-05-18T02:41:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8yTqOQCZY5NFKT99SCT3ycvS+G3fCKgPnlVJtcMeCCO1ywpfUqGKOPsOhRnuPKrmwzpz6OPRcb4YKn/7oF0KAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T09:08:43.634233Z"},"content_sha256":"8b116b5e1e56c15ab26604360d6358dd7de44b18cec05bf44b12c6f5c3ab7808","schema_version":"1.0","event_id":"sha256:8b116b5e1e56c15ab26604360d6358dd7de44b18cec05bf44b12c6f5c3ab7808"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:HWEMPUW6ACO3GDPPVGNN73V2Z3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Deep Graph Embedding Network Model for Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chu He, Teng Yang, Yufei Gan","submitted_at":"2014-09-25T16:14:18Z","abstract_excerpt":"In this paper, we propose a new deep learning network \"GENet\", it combines the multi-layer network architec- ture and graph embedding framework. Firstly, we use simplest unsupervised learning PCA/LDA as first layer to generate the low- level feature. Secondly, many cascaded dimensionality reduction layers based on graph embedding framework are applied to GENet. Finally, a linear SVM classifier is used to classify dimension-reduced features. The experiments indicate that higher classification accuracy can be obtained by this algorithm on the CMU-PIE, ORL, Extended Yale B dataset."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.7313","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":"2026-05-18T02:41:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+HyIumL9QKXvq/GLEOn9T/ofUZZrWNi7p0d+07r1MVgnbLCuU6ZwN3g02thj4UAAbRaXKqF4Rxft+ElF0bLuDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T09:08:43.634584Z"},"content_sha256":"e9635c6fce82228ebf920c5dd2852f346edc7674140f3db1e6da0fe54cc55daf","schema_version":"1.0","event_id":"sha256:e9635c6fce82228ebf920c5dd2852f346edc7674140f3db1e6da0fe54cc55daf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HWEMPUW6ACO3GDPPVGNN73V2Z3/bundle.json","state_url":"https://pith.science/pith/HWEMPUW6ACO3GDPPVGNN73V2Z3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HWEMPUW6ACO3GDPPVGNN73V2Z3/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":"2026-06-05T09:08:43Z","links":{"resolver":"https://pith.science/pith/HWEMPUW6ACO3GDPPVGNN73V2Z3","bundle":"https://pith.science/pith/HWEMPUW6ACO3GDPPVGNN73V2Z3/bundle.json","state":"https://pith.science/pith/HWEMPUW6ACO3GDPPVGNN73V2Z3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HWEMPUW6ACO3GDPPVGNN73V2Z3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:HWEMPUW6ACO3GDPPVGNN73V2Z3","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":"65a65854848e8896148c04eed2e3cc4ae936e443ee3e3489ae9d7950740ed28c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-09-25T16:14:18Z","title_canon_sha256":"2aaca1d1b16ff693b551988c614f24e7371c47cd0511858ac890562e67c4349a"},"schema_version":"1.0","source":{"id":"1409.7313","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.7313","created_at":"2026-05-18T02:41:58Z"},{"alias_kind":"arxiv_version","alias_value":"1409.7313v1","created_at":"2026-05-18T02:41:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.7313","created_at":"2026-05-18T02:41:58Z"},{"alias_kind":"pith_short_12","alias_value":"HWEMPUW6ACO3","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"HWEMPUW6ACO3GDPP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"HWEMPUW6","created_at":"2026-05-18T12:28:30Z"}],"graph_snapshots":[{"event_id":"sha256:e9635c6fce82228ebf920c5dd2852f346edc7674140f3db1e6da0fe54cc55daf","target":"graph","created_at":"2026-05-18T02:41:58Z","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 paper, we propose a new deep learning network \"GENet\", it combines the multi-layer network architec- ture and graph embedding framework. Firstly, we use simplest unsupervised learning PCA/LDA as first layer to generate the low- level feature. Secondly, many cascaded dimensionality reduction layers based on graph embedding framework are applied to GENet. Finally, a linear SVM classifier is used to classify dimension-reduced features. The experiments indicate that higher classification accuracy can be obtained by this algorithm on the CMU-PIE, ORL, Extended Yale B dataset.","authors_text":"Chu He, Teng Yang, Yufei Gan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-09-25T16:14:18Z","title":"A Deep Graph Embedding Network Model for Face Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.7313","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":"sha256:8b116b5e1e56c15ab26604360d6358dd7de44b18cec05bf44b12c6f5c3ab7808","target":"record","created_at":"2026-05-18T02:41:58Z","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":"65a65854848e8896148c04eed2e3cc4ae936e443ee3e3489ae9d7950740ed28c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-09-25T16:14:18Z","title_canon_sha256":"2aaca1d1b16ff693b551988c614f24e7371c47cd0511858ac890562e67c4349a"},"schema_version":"1.0","source":{"id":"1409.7313","kind":"arxiv","version":1}},"canonical_sha256":"3d88c7d2de009db30defa99adfeebacef82b04af51c791b733bf15960ee72a8b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d88c7d2de009db30defa99adfeebacef82b04af51c791b733bf15960ee72a8b","first_computed_at":"2026-05-18T02:41:58.691394Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:41:58.691394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RlZjdbuRl8S/ac5qgtbem87OXuILYQZwnHE6l8PNhlyUK+/RPZAhdoock8NwD81gXryWSSPnZ4cCmA/WNvvSDA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:41:58.691810Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.7313","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b116b5e1e56c15ab26604360d6358dd7de44b18cec05bf44b12c6f5c3ab7808","sha256:e9635c6fce82228ebf920c5dd2852f346edc7674140f3db1e6da0fe54cc55daf"],"state_sha256":"b6fb7e20c0fb2fc27d4e6f3a8e01fce8b67bda3003be235d26439baeb8a5d3d9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jd7Mx8EQCBf9BraCJoneFk2WhLwmTpwSPSxvTACLgqfqNTmNVuZlz6lP5AzTSCjuxha3hD0JCHELg4OSHHgdCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T09:08:43.636581Z","bundle_sha256":"da5d62330ad6ebb0e6b4745ac7a78f23e0a7d879c805063df0d5a92e4a184598"}}