{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:6YHG4QP4BSXHVCFVXRS74YBFFP","short_pith_number":"pith:6YHG4QP4","canonical_record":{"source":{"id":"1405.1207","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T09:46:28Z","cross_cats_sorted":[],"title_canon_sha256":"926f2a7e606a90a61ab6116415b38ed2fbf1e9b4a3e960703c785314acaf2233","abstract_canon_sha256":"a922151ed32de127607c21be79e5b3734dc6f8b1d400e726b959a38cbfe40c53"},"schema_version":"1.0"},"canonical_sha256":"f60e6e41fc0cae7a88b5bc65fe60252bc0aa1f926c9e3deb3ba8647f4f9cb21f","source":{"kind":"arxiv","id":"1405.1207","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.1207","created_at":"2026-05-18T02:52:33Z"},{"alias_kind":"arxiv_version","alias_value":"1405.1207v1","created_at":"2026-05-18T02:52:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.1207","created_at":"2026-05-18T02:52:33Z"},{"alias_kind":"pith_short_12","alias_value":"6YHG4QP4BSXH","created_at":"2026-05-18T12:28:16Z"},{"alias_kind":"pith_short_16","alias_value":"6YHG4QP4BSXHVCFV","created_at":"2026-05-18T12:28:16Z"},{"alias_kind":"pith_short_8","alias_value":"6YHG4QP4","created_at":"2026-05-18T12:28:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:6YHG4QP4BSXHVCFVXRS74YBFFP","target":"record","payload":{"canonical_record":{"source":{"id":"1405.1207","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T09:46:28Z","cross_cats_sorted":[],"title_canon_sha256":"926f2a7e606a90a61ab6116415b38ed2fbf1e9b4a3e960703c785314acaf2233","abstract_canon_sha256":"a922151ed32de127607c21be79e5b3734dc6f8b1d400e726b959a38cbfe40c53"},"schema_version":"1.0"},"canonical_sha256":"f60e6e41fc0cae7a88b5bc65fe60252bc0aa1f926c9e3deb3ba8647f4f9cb21f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:33.284442Z","signature_b64":"bKqF439RdTsFXv+zloqT6G9wY0lAmmu4KXmMZuOD7x6AoLuA9jzKyS4ZsXq3jpFWJ+hXW5Wk2u8FJ4LJmZHiDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f60e6e41fc0cae7a88b5bc65fe60252bc0aa1f926c9e3deb3ba8647f4f9cb21f","last_reissued_at":"2026-05-18T02:52:33.283980Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:33.283980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1405.1207","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":"2026-05-18T02:52:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YbFhUvy+DxB5j76FE3YiIuU8tPBhnEnaVkawaPvw9wgGM90KjsnnLEXK8E7Kjf9QP4lrnsvcAk73nvr2395YAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:46:32.437450Z"},"content_sha256":"536451066aa3a4f79b7c1b5d12a332825047b3aa698329fedc1e712103a29af1","schema_version":"1.0","event_id":"sha256:536451066aa3a4f79b7c1b5d12a332825047b3aa698329fedc1e712103a29af1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:6YHG4QP4BSXHVCFVXRS74YBFFP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fanlong Zhang, Jianjun Qian, Jian Yang, Lei Luo, Yicheng Gao","submitted_at":"2014-05-06T09:46:28Z","abstract_excerpt":"Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus neglects the whole structure of the error image. We observe that occlusion and illumination changes generally lead to a low-rank error image. To make use of this low-rank structural information, this paper presents a two-dimensional image matrix based error model, i.e. matrix regression, for face representation and classification. Our model uses the minimal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.1207","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":"2026-05-18T02:52:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"61ughBqLK58jQGoIajSx5zUjQgCl7bqi0zyJVNHkxoopbl2WgvEHI+lugGt5+YeYPX6lf12dH8qMaJCegm0pCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:46:32.437795Z"},"content_sha256":"2af1ca28f858ff74d167880f20e8e526c5bfa46f45c5ff9fbb7b4f44a9e5b4ba","schema_version":"1.0","event_id":"sha256:2af1ca28f858ff74d167880f20e8e526c5bfa46f45c5ff9fbb7b4f44a9e5b4ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6YHG4QP4BSXHVCFVXRS74YBFFP/bundle.json","state_url":"https://pith.science/pith/6YHG4QP4BSXHVCFVXRS74YBFFP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6YHG4QP4BSXHVCFVXRS74YBFFP/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":"2026-06-08T14:46:32Z","links":{"resolver":"https://pith.science/pith/6YHG4QP4BSXHVCFVXRS74YBFFP","bundle":"https://pith.science/pith/6YHG4QP4BSXHVCFVXRS74YBFFP/bundle.json","state":"https://pith.science/pith/6YHG4QP4BSXHVCFVXRS74YBFFP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6YHG4QP4BSXHVCFVXRS74YBFFP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:6YHG4QP4BSXHVCFVXRS74YBFFP","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":"a922151ed32de127607c21be79e5b3734dc6f8b1d400e726b959a38cbfe40c53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T09:46:28Z","title_canon_sha256":"926f2a7e606a90a61ab6116415b38ed2fbf1e9b4a3e960703c785314acaf2233"},"schema_version":"1.0","source":{"id":"1405.1207","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.1207","created_at":"2026-05-18T02:52:33Z"},{"alias_kind":"arxiv_version","alias_value":"1405.1207v1","created_at":"2026-05-18T02:52:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.1207","created_at":"2026-05-18T02:52:33Z"},{"alias_kind":"pith_short_12","alias_value":"6YHG4QP4BSXH","created_at":"2026-05-18T12:28:16Z"},{"alias_kind":"pith_short_16","alias_value":"6YHG4QP4BSXHVCFV","created_at":"2026-05-18T12:28:16Z"},{"alias_kind":"pith_short_8","alias_value":"6YHG4QP4","created_at":"2026-05-18T12:28:16Z"}],"graph_snapshots":[{"event_id":"sha256:2af1ca28f858ff74d167880f20e8e526c5bfa46f45c5ff9fbb7b4f44a9e5b4ba","target":"graph","created_at":"2026-05-18T02:52:33Z","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":"Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus neglects the whole structure of the error image. We observe that occlusion and illumination changes generally lead to a low-rank error image. To make use of this low-rank structural information, this paper presents a two-dimensional image matrix based error model, i.e. matrix regression, for face representation and classification. Our model uses the minimal","authors_text":"Fanlong Zhang, Jianjun Qian, Jian Yang, Lei Luo, Yicheng Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T09:46:28Z","title":"Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.1207","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":"sha256:536451066aa3a4f79b7c1b5d12a332825047b3aa698329fedc1e712103a29af1","target":"record","created_at":"2026-05-18T02:52:33Z","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":"a922151ed32de127607c21be79e5b3734dc6f8b1d400e726b959a38cbfe40c53","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-05-06T09:46:28Z","title_canon_sha256":"926f2a7e606a90a61ab6116415b38ed2fbf1e9b4a3e960703c785314acaf2233"},"schema_version":"1.0","source":{"id":"1405.1207","kind":"arxiv","version":1}},"canonical_sha256":"f60e6e41fc0cae7a88b5bc65fe60252bc0aa1f926c9e3deb3ba8647f4f9cb21f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f60e6e41fc0cae7a88b5bc65fe60252bc0aa1f926c9e3deb3ba8647f4f9cb21f","first_computed_at":"2026-05-18T02:52:33.283980Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:52:33.283980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bKqF439RdTsFXv+zloqT6G9wY0lAmmu4KXmMZuOD7x6AoLuA9jzKyS4ZsXq3jpFWJ+hXW5Wk2u8FJ4LJmZHiDw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:52:33.284442Z","signed_message":"canonical_sha256_bytes"},"source_id":"1405.1207","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:536451066aa3a4f79b7c1b5d12a332825047b3aa698329fedc1e712103a29af1","sha256:2af1ca28f858ff74d167880f20e8e526c5bfa46f45c5ff9fbb7b4f44a9e5b4ba"],"state_sha256":"9dee7853ed18cf101f0d93c3b106b8d5637c509e5e9486b5599deb2b9d50a940"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Aqp6E+a433TgEfWMllqFhnxqH68+k95KSoSnEFHNnF1t6SxrCqzdfdvdGXaaNwFw1HHcAoHReuGa3EQEwPLPDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T14:46:32.439617Z","bundle_sha256":"f8b6645f5a92c64c61f156c90e7b170ed96ad7bbad87d044536ab8216ae866ae"}}