{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WHGXJJF32INXGB5VZKNNJASWG5","short_pith_number":"pith:WHGXJJF3","canonical_record":{"source":{"id":"2403.16966","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-03-25T17:25:44Z","cross_cats_sorted":[],"title_canon_sha256":"fecca6f09ac13333e6e56559a5bdf3008cc5d279ad998beabc6cedbb95b198d7","abstract_canon_sha256":"256839ddce783747c9215cff9583032102b8fb1cbb96dc6914db381faa7ba360"},"schema_version":"1.0"},"canonical_sha256":"b1cd74a4bbd21b7307b5ca9ad482563768dd83fecbaa17b5140ac4c405c44d21","source":{"kind":"arxiv","id":"2403.16966","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.16966","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"arxiv_version","alias_value":"2403.16966v1","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.16966","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"pith_short_12","alias_value":"WHGXJJF32INX","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"pith_short_16","alias_value":"WHGXJJF32INXGB5V","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"pith_short_8","alias_value":"WHGXJJF3","created_at":"2026-07-05T08:00:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WHGXJJF32INXGB5VZKNNJASWG5","target":"record","payload":{"canonical_record":{"source":{"id":"2403.16966","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-03-25T17:25:44Z","cross_cats_sorted":[],"title_canon_sha256":"fecca6f09ac13333e6e56559a5bdf3008cc5d279ad998beabc6cedbb95b198d7","abstract_canon_sha256":"256839ddce783747c9215cff9583032102b8fb1cbb96dc6914db381faa7ba360"},"schema_version":"1.0"},"canonical_sha256":"b1cd74a4bbd21b7307b5ca9ad482563768dd83fecbaa17b5140ac4c405c44d21","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:00:23.230873Z","signature_b64":"uYNBNmZ02MEHtomKk72QKRbDzgYFb4fEzLOTOmhWm5uAsVzDTDnT4jnrTPfO191Zg423lAYzCk23sekqiHPBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1cd74a4bbd21b7307b5ca9ad482563768dd83fecbaa17b5140ac4c405c44d21","last_reissued_at":"2026-07-05T08:00:23.230408Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:00:23.230408Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.16966","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":"2026-07-05T08:00:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t7nwdOmJd06uz96DYCzqcc/fHCU1cudBP/HGPpDp+bv0d60qlDxH2rgIHJku1cKSfFkOUH/8yZvvzV9ezM9kAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:45:13.578686Z"},"content_sha256":"d2e86a65c5307769104c81b7d423f59b5d4d1dcb8806a8072e7f3f53a73e8e09","schema_version":"1.0","event_id":"sha256:d2e86a65c5307769104c81b7d423f59b5d4d1dcb8806a8072e7f3f53a73e8e09"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WHGXJJF32INXGB5VZKNNJASWG5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Defeng Sun, Liping Zhang, Yan Li","submitted_at":"2024-03-25T17:25:44Z","abstract_excerpt":"Unsupervised feature selection has drawn wide attention in the era of big data since it is a primary technique for dimensionality reduction. However, many existing unsupervised feature selection models and solution methods were presented for the purpose of application, and lack of theoretical support, e.g., without convergence analysis. In this paper, we first establish a novel unsupervised feature selection model based on regularized minimization with nonnegative orthogonal constraints, which has advantages of embedding feature selection into the nonnegative spectral clustering and preventing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.16966","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/2403.16966/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":"2026-07-05T08:00:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ou4xnEBhw8wbKoUJYF2/e+pt46nxZX474gsM23cUeUg3X32U7/QFWw9R5X4ugbHURysBaooSWl4iRI5hmL70CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T06:45:13.579204Z"},"content_sha256":"7b40a131642841458ab41318e2cc9144b0501d531a8121b560936bfbc5b110bf","schema_version":"1.0","event_id":"sha256:7b40a131642841458ab41318e2cc9144b0501d531a8121b560936bfbc5b110bf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WHGXJJF32INXGB5VZKNNJASWG5/bundle.json","state_url":"https://pith.science/pith/WHGXJJF32INXGB5VZKNNJASWG5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WHGXJJF32INXGB5VZKNNJASWG5/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":"2026-08-15T06:45:13Z","links":{"resolver":"https://pith.science/pith/WHGXJJF32INXGB5VZKNNJASWG5","bundle":"https://pith.science/pith/WHGXJJF32INXGB5VZKNNJASWG5/bundle.json","state":"https://pith.science/pith/WHGXJJF32INXGB5VZKNNJASWG5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WHGXJJF32INXGB5VZKNNJASWG5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WHGXJJF32INXGB5VZKNNJASWG5","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":"256839ddce783747c9215cff9583032102b8fb1cbb96dc6914db381faa7ba360","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-03-25T17:25:44Z","title_canon_sha256":"fecca6f09ac13333e6e56559a5bdf3008cc5d279ad998beabc6cedbb95b198d7"},"schema_version":"1.0","source":{"id":"2403.16966","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.16966","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"arxiv_version","alias_value":"2403.16966v1","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.16966","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"pith_short_12","alias_value":"WHGXJJF32INX","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"pith_short_16","alias_value":"WHGXJJF32INXGB5V","created_at":"2026-07-05T08:00:23Z"},{"alias_kind":"pith_short_8","alias_value":"WHGXJJF3","created_at":"2026-07-05T08:00:23Z"}],"graph_snapshots":[{"event_id":"sha256:7b40a131642841458ab41318e2cc9144b0501d531a8121b560936bfbc5b110bf","target":"graph","created_at":"2026-07-05T08:00:23Z","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/2403.16966/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Unsupervised feature selection has drawn wide attention in the era of big data since it is a primary technique for dimensionality reduction. However, many existing unsupervised feature selection models and solution methods were presented for the purpose of application, and lack of theoretical support, e.g., without convergence analysis. In this paper, we first establish a novel unsupervised feature selection model based on regularized minimization with nonnegative orthogonal constraints, which has advantages of embedding feature selection into the nonnegative spectral clustering and preventing","authors_text":"Defeng Sun, Liping Zhang, Yan Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-03-25T17:25:44Z","title":"Unsupervised Feature Selection via Nonnegative Orthogonal Constrained Regularized Minimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.16966","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:d2e86a65c5307769104c81b7d423f59b5d4d1dcb8806a8072e7f3f53a73e8e09","target":"record","created_at":"2026-07-05T08:00:23Z","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":"256839ddce783747c9215cff9583032102b8fb1cbb96dc6914db381faa7ba360","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-03-25T17:25:44Z","title_canon_sha256":"fecca6f09ac13333e6e56559a5bdf3008cc5d279ad998beabc6cedbb95b198d7"},"schema_version":"1.0","source":{"id":"2403.16966","kind":"arxiv","version":1}},"canonical_sha256":"b1cd74a4bbd21b7307b5ca9ad482563768dd83fecbaa17b5140ac4c405c44d21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b1cd74a4bbd21b7307b5ca9ad482563768dd83fecbaa17b5140ac4c405c44d21","first_computed_at":"2026-07-05T08:00:23.230408Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:00:23.230408Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uYNBNmZ02MEHtomKk72QKRbDzgYFb4fEzLOTOmhWm5uAsVzDTDnT4jnrTPfO191Zg423lAYzCk23sekqiHPBBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:00:23.230873Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.16966","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2e86a65c5307769104c81b7d423f59b5d4d1dcb8806a8072e7f3f53a73e8e09","sha256:7b40a131642841458ab41318e2cc9144b0501d531a8121b560936bfbc5b110bf"],"state_sha256":"8b6f2741484ba829911b4e9625c52242ff29f68555bca3c1929456b149f5a267"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dkv5nv769sP1ghI6T4MB6LSuaUqJ0z0PhyX3X76+sNanICjoP24YUYZigVCkVAVyadYNULmcPnb3pPCBZy2ADg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T06:45:13.582801Z","bundle_sha256":"ca587ce509112e04f90ecfeb4e1719cc9c62400db86f0a4afadb6dee65178045"}}