{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QASCNFDPJD7GXGWQZWNFD4F3WL","short_pith_number":"pith:QASCNFDP","canonical_record":{"source":{"id":"1808.06638","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2018-08-20T18:18:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"75c7e550d28f1ec7a73797d64b344a03c7327a1fef3f6406246542d329b6525b","abstract_canon_sha256":"50be1a83016dd0e4bc7972cb9b090abd8067e9fbfe8d0de74600ca645fe0e4ed"},"schema_version":"1.0"},"canonical_sha256":"802426946f48fe6b9ad0cd9a51f0bbb2d23bdd4eb4ba31b1c4ce8a13cd77acbe","source":{"kind":"arxiv","id":"1808.06638","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06638","created_at":"2026-05-18T00:07:27Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06638v3","created_at":"2026-05-18T00:07:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06638","created_at":"2026-05-18T00:07:27Z"},{"alias_kind":"pith_short_12","alias_value":"QASCNFDPJD7G","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QASCNFDPJD7GXGWQ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QASCNFDP","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QASCNFDPJD7GXGWQZWNFD4F3WL","target":"record","payload":{"canonical_record":{"source":{"id":"1808.06638","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2018-08-20T18:18:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"75c7e550d28f1ec7a73797d64b344a03c7327a1fef3f6406246542d329b6525b","abstract_canon_sha256":"50be1a83016dd0e4bc7972cb9b090abd8067e9fbfe8d0de74600ca645fe0e4ed"},"schema_version":"1.0"},"canonical_sha256":"802426946f48fe6b9ad0cd9a51f0bbb2d23bdd4eb4ba31b1c4ce8a13cd77acbe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:27.251624Z","signature_b64":"hl/Bk5SaRz+g/AoqWCxEmtCL9oRgoqwrhd3mf8nkIR4RTOQQopLWQzzeDezTToyJ2m9CA7OzEQedKXsH81IjAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"802426946f48fe6b9ad0cd9a51f0bbb2d23bdd4eb4ba31b1c4ce8a13cd77acbe","last_reissued_at":"2026-05-18T00:07:27.250994Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:27.250994Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.06638","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fAyTttAW8e8hCp070G6L7n6+6KhrIcX6GFB2P+sc6hvMUQPY2+dEskb5O9knSJ1SBqUQgf2ehbYSe9Djxi+kAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T15:47:49.413285Z"},"content_sha256":"9053ff2197fa787aaff7634b1f7821243800ebc60e177b8aa7bd1e144874a3cc","schema_version":"1.0","event_id":"sha256:9053ff2197fa787aaff7634b1f7821243800ebc60e177b8aa7bd1e144874a3cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QASCNFDPJD7GXGWQZWNFD4F3WL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Supervised Kernel PCA For Longitudinal Data","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Gregory A. Ryslik, Min Ouyang, Patrick Staples, Paul Dagum, Robert F. Dougherty","submitted_at":"2018-08-20T18:18:52Z","abstract_excerpt":"In statistical learning, high covariate dimensionality poses challenges for robust prediction and inference. To address this challenge, supervised dimension reduction is often performed, where dependence on the outcome is maximized for a selected covariate subspace with smaller dimensionality. Prevalent dimension reduction techniques assume data are $i.i.d.$, which is not appropriate for longitudinal data comprising multiple subjects with repeated measurements over time. In this paper, we derive a decomposition of the Hilbert-Schmidt Independence Criterion as a supervised loss function for lon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06638","kind":"arxiv","version":3},"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":"2026-05-18T00:07:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fZriQOHqvRKNgsKFRQ6nQYbwRdkilfKMca/EfVPI2KktOLr948gtJ9+p6xBjbMauGuAzs/tLYVYkCWSHWtybBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T15:47:49.413740Z"},"content_sha256":"b11da26ff0b55ccce950d0e983dc95775012b415ec363767bef840662f17d456","schema_version":"1.0","event_id":"sha256:b11da26ff0b55ccce950d0e983dc95775012b415ec363767bef840662f17d456"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QASCNFDPJD7GXGWQZWNFD4F3WL/bundle.json","state_url":"https://pith.science/pith/QASCNFDPJD7GXGWQZWNFD4F3WL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QASCNFDPJD7GXGWQZWNFD4F3WL/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":"2026-05-27T15:47:49Z","links":{"resolver":"https://pith.science/pith/QASCNFDPJD7GXGWQZWNFD4F3WL","bundle":"https://pith.science/pith/QASCNFDPJD7GXGWQZWNFD4F3WL/bundle.json","state":"https://pith.science/pith/QASCNFDPJD7GXGWQZWNFD4F3WL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QASCNFDPJD7GXGWQZWNFD4F3WL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QASCNFDPJD7GXGWQZWNFD4F3WL","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":"50be1a83016dd0e4bc7972cb9b090abd8067e9fbfe8d0de74600ca645fe0e4ed","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2018-08-20T18:18:52Z","title_canon_sha256":"75c7e550d28f1ec7a73797d64b344a03c7327a1fef3f6406246542d329b6525b"},"schema_version":"1.0","source":{"id":"1808.06638","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.06638","created_at":"2026-05-18T00:07:27Z"},{"alias_kind":"arxiv_version","alias_value":"1808.06638v3","created_at":"2026-05-18T00:07:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06638","created_at":"2026-05-18T00:07:27Z"},{"alias_kind":"pith_short_12","alias_value":"QASCNFDPJD7G","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QASCNFDPJD7GXGWQ","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QASCNFDP","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:b11da26ff0b55ccce950d0e983dc95775012b415ec363767bef840662f17d456","target":"graph","created_at":"2026-05-18T00:07:27Z","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 statistical learning, high covariate dimensionality poses challenges for robust prediction and inference. To address this challenge, supervised dimension reduction is often performed, where dependence on the outcome is maximized for a selected covariate subspace with smaller dimensionality. Prevalent dimension reduction techniques assume data are $i.i.d.$, which is not appropriate for longitudinal data comprising multiple subjects with repeated measurements over time. In this paper, we derive a decomposition of the Hilbert-Schmidt Independence Criterion as a supervised loss function for lon","authors_text":"Gregory A. Ryslik, Min Ouyang, Patrick Staples, Paul Dagum, Robert F. Dougherty","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2018-08-20T18:18:52Z","title":"Supervised Kernel PCA For Longitudinal Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06638","kind":"arxiv","version":3},"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":"sha256:9053ff2197fa787aaff7634b1f7821243800ebc60e177b8aa7bd1e144874a3cc","target":"record","created_at":"2026-05-18T00:07:27Z","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":"50be1a83016dd0e4bc7972cb9b090abd8067e9fbfe8d0de74600ca645fe0e4ed","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ME","submitted_at":"2018-08-20T18:18:52Z","title_canon_sha256":"75c7e550d28f1ec7a73797d64b344a03c7327a1fef3f6406246542d329b6525b"},"schema_version":"1.0","source":{"id":"1808.06638","kind":"arxiv","version":3}},"canonical_sha256":"802426946f48fe6b9ad0cd9a51f0bbb2d23bdd4eb4ba31b1c4ce8a13cd77acbe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"802426946f48fe6b9ad0cd9a51f0bbb2d23bdd4eb4ba31b1c4ce8a13cd77acbe","first_computed_at":"2026-05-18T00:07:27.250994Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:27.250994Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hl/Bk5SaRz+g/AoqWCxEmtCL9oRgoqwrhd3mf8nkIR4RTOQQopLWQzzeDezTToyJ2m9CA7OzEQedKXsH81IjAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:27.251624Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.06638","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9053ff2197fa787aaff7634b1f7821243800ebc60e177b8aa7bd1e144874a3cc","sha256:b11da26ff0b55ccce950d0e983dc95775012b415ec363767bef840662f17d456"],"state_sha256":"86d0697a7cac3232d20f21f8bf0184e4d82cd9238c38880202ac91b5544e17c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TzQGFaNoAz8eOZTF7AFOcKrAXJtv4Z0FlOPvnqxmp6YGdI/0w7MA+bQeWrHrbSK6a06HnHvIIZn9yCYWl5kmBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T15:47:49.416897Z","bundle_sha256":"f71c394290709edd948b1b6534473afaa59f093c3eb1f7933e2e7b8996d27ec9"}}