{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZVIBUYUVWBLKXZEPUZDP66I5V7","short_pith_number":"pith:ZVIBUYUV","canonical_record":{"source":{"id":"1710.06229","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-17T12:00:45Z","cross_cats_sorted":[],"title_canon_sha256":"9035847e350bba47661ea76470389f8d52f2a2e1a9576eab04e0a46ad6eb5952","abstract_canon_sha256":"5dd46d7df41447c5a3bfea570ac14e126b18db5bb33e3dc78a9fa03682fb8cf8"},"schema_version":"1.0"},"canonical_sha256":"cd501a6295b056abe48fa646ff791dafd2b3e991eef2bdc8c8358f5c89e1f703","source":{"kind":"arxiv","id":"1710.06229","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.06229","created_at":"2026-05-18T00:03:32Z"},{"alias_kind":"arxiv_version","alias_value":"1710.06229v1","created_at":"2026-05-18T00:03:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06229","created_at":"2026-05-18T00:03:32Z"},{"alias_kind":"pith_short_12","alias_value":"ZVIBUYUVWBLK","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZVIBUYUVWBLKXZEP","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZVIBUYUV","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZVIBUYUVWBLKXZEPUZDP66I5V7","target":"record","payload":{"canonical_record":{"source":{"id":"1710.06229","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-17T12:00:45Z","cross_cats_sorted":[],"title_canon_sha256":"9035847e350bba47661ea76470389f8d52f2a2e1a9576eab04e0a46ad6eb5952","abstract_canon_sha256":"5dd46d7df41447c5a3bfea570ac14e126b18db5bb33e3dc78a9fa03682fb8cf8"},"schema_version":"1.0"},"canonical_sha256":"cd501a6295b056abe48fa646ff791dafd2b3e991eef2bdc8c8358f5c89e1f703","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:32.432268Z","signature_b64":"zcHFyAJJuL5HvfRssChFYXNjNCXq2i4hIKg1JQ/rnv0XOo1lfG7Lv3R0delZEwKWk8mfU4eaI6WK5X01vOLXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd501a6295b056abe48fa646ff791dafd2b3e991eef2bdc8c8358f5c89e1f703","last_reissued_at":"2026-05-18T00:03:32.431616Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:32.431616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.06229","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":"2026-05-18T00:03:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4eV1ig/4CPn74yNSVvWdeg1AWyBAUZ1LzkqctdknIQ34FagJ+CoI+obD0A+th5KJ+qFo5iq7TsZeDi+SP2PxAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T10:08:09.307808Z"},"content_sha256":"8b46e06b9ee1788d95f71a5af38c94f036375337d174a41aa6f73f8052c5de13","schema_version":"1.0","event_id":"sha256:8b46e06b9ee1788d95f71a5af38c94f036375337d174a41aa6f73f8052c5de13"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZVIBUYUVWBLKXZEPUZDP66I5V7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Iterative Supervised Principal Components","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Aki Vehtari, Juho Piironen","submitted_at":"2017-10-17T12:00:45Z","abstract_excerpt":"In high-dimensional prediction problems, where the number of features may greatly exceed the number of training instances, fully Bayesian approach with a sparsifying prior is known to produce good results but is computationally challenging. To alleviate this computational burden, we propose to use a preprocessing step where we first apply a dimension reduction to the original data to reduce the number of features to something that is computationally conveniently handled by Bayesian methods. To do this, we propose a new dimension reduction technique, called iterative supervised principal compon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06229","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":"2026-05-18T00:03:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gbto+IuSdoP9NHqSK+r5rVJG3DvZXV9GbRn+rhlDL05E89eve3zNqVnObIyuUkEk2f3mi0GqUSpW0Br/8jI4DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T10:08:09.308151Z"},"content_sha256":"ee935efc0054b9ef9d82ffdb6962c5bc6db9a309c93c2563e310b371691584ee","schema_version":"1.0","event_id":"sha256:ee935efc0054b9ef9d82ffdb6962c5bc6db9a309c93c2563e310b371691584ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZVIBUYUVWBLKXZEPUZDP66I5V7/bundle.json","state_url":"https://pith.science/pith/ZVIBUYUVWBLKXZEPUZDP66I5V7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZVIBUYUVWBLKXZEPUZDP66I5V7/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":"2026-05-28T10:08:09Z","links":{"resolver":"https://pith.science/pith/ZVIBUYUVWBLKXZEPUZDP66I5V7","bundle":"https://pith.science/pith/ZVIBUYUVWBLKXZEPUZDP66I5V7/bundle.json","state":"https://pith.science/pith/ZVIBUYUVWBLKXZEPUZDP66I5V7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZVIBUYUVWBLKXZEPUZDP66I5V7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZVIBUYUVWBLKXZEPUZDP66I5V7","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":"5dd46d7df41447c5a3bfea570ac14e126b18db5bb33e3dc78a9fa03682fb8cf8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-17T12:00:45Z","title_canon_sha256":"9035847e350bba47661ea76470389f8d52f2a2e1a9576eab04e0a46ad6eb5952"},"schema_version":"1.0","source":{"id":"1710.06229","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.06229","created_at":"2026-05-18T00:03:32Z"},{"alias_kind":"arxiv_version","alias_value":"1710.06229v1","created_at":"2026-05-18T00:03:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06229","created_at":"2026-05-18T00:03:32Z"},{"alias_kind":"pith_short_12","alias_value":"ZVIBUYUVWBLK","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZVIBUYUVWBLKXZEP","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZVIBUYUV","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:ee935efc0054b9ef9d82ffdb6962c5bc6db9a309c93c2563e310b371691584ee","target":"graph","created_at":"2026-05-18T00:03:32Z","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 high-dimensional prediction problems, where the number of features may greatly exceed the number of training instances, fully Bayesian approach with a sparsifying prior is known to produce good results but is computationally challenging. To alleviate this computational burden, we propose to use a preprocessing step where we first apply a dimension reduction to the original data to reduce the number of features to something that is computationally conveniently handled by Bayesian methods. To do this, we propose a new dimension reduction technique, called iterative supervised principal compon","authors_text":"Aki Vehtari, Juho Piironen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-17T12:00:45Z","title":"Iterative Supervised Principal Components"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06229","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":"sha256:8b46e06b9ee1788d95f71a5af38c94f036375337d174a41aa6f73f8052c5de13","target":"record","created_at":"2026-05-18T00:03:32Z","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":"5dd46d7df41447c5a3bfea570ac14e126b18db5bb33e3dc78a9fa03682fb8cf8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-10-17T12:00:45Z","title_canon_sha256":"9035847e350bba47661ea76470389f8d52f2a2e1a9576eab04e0a46ad6eb5952"},"schema_version":"1.0","source":{"id":"1710.06229","kind":"arxiv","version":1}},"canonical_sha256":"cd501a6295b056abe48fa646ff791dafd2b3e991eef2bdc8c8358f5c89e1f703","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd501a6295b056abe48fa646ff791dafd2b3e991eef2bdc8c8358f5c89e1f703","first_computed_at":"2026-05-18T00:03:32.431616Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:32.431616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zcHFyAJJuL5HvfRssChFYXNjNCXq2i4hIKg1JQ/rnv0XOo1lfG7Lv3R0delZEwKWk8mfU4eaI6WK5X01vOLXDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:32.432268Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.06229","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b46e06b9ee1788d95f71a5af38c94f036375337d174a41aa6f73f8052c5de13","sha256:ee935efc0054b9ef9d82ffdb6962c5bc6db9a309c93c2563e310b371691584ee"],"state_sha256":"2acfb77e579ac4e9a1fd15375254e01ba62bdff3f2d6e5cec2a9a3ab813b05d6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9mE6Hf5Bd2kcST9IVhDaRrl2r6hwOm1rvPIdZD7kBbp4z0M3j7HReWHcPex2/tOJOJgX0QvAHd0hwS0fPKHbBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T10:08:09.310136Z","bundle_sha256":"727c27db9ba670be8f9f8369ad60dba1646d8e230f1448305f956d24abe1dc57"}}