{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YWFUQRD5K7CIZ5KJ2X2JFE7VPU","short_pith_number":"pith:YWFUQRD5","schema_version":"1.0","canonical_sha256":"c58b48447d57c48cf549d5f49293f57d3a72a22a8db6726c0151e7cdbe220bf3","source":{"kind":"arxiv","id":"2607.21741","version":1},"attestation_state":"computed","paper":{"title":"Globally aligned Principal Component Analysis for multi-group data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Federico Severino, Hedayat Fathi, Marzia A. Cremona","submitted_at":"2026-07-23T18:47:21Z","abstract_excerpt":"We propose a novel principal component analysis (PCA) for multi-group datasets, where the same numerical variables are measured across different groups of observations. Existing approaches either ignore group structure entirely by working with global (pooled) data, focus exclusively on local structure (group-wise PCA), or impose restrictive assumptions of common principal components. Our approach respects the multi-group nature of data while improving global comparability of components. We combine group-specific principal components with global ones through an explicit alignment mechanism base"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.21741","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-07-23T18:47:21Z","cross_cats_sorted":[],"title_canon_sha256":"c6e3fd7e781e4b3b7ae2bd578c42f4aa6eca0e868f7fe2f6917a7d89927762f6","abstract_canon_sha256":"164b169b7353360c69a5676174170ba03b5ad7866c3d4c697d0c5048606ca5e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T00:20:18.452263Z","signature_b64":"PdlEgEOpXw2ezTcutJsFJ3AXrHGZbMmQUHZBzAvVFh3BhTO8pWAsXMy5omMmtN5ojIPujEFSFs2xTE+l+NJeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c58b48447d57c48cf549d5f49293f57d3a72a22a8db6726c0151e7cdbe220bf3","last_reissued_at":"2026-07-27T00:20:18.451442Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T00:20:18.451442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Globally aligned Principal Component Analysis for multi-group data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Federico Severino, Hedayat Fathi, Marzia A. Cremona","submitted_at":"2026-07-23T18:47:21Z","abstract_excerpt":"We propose a novel principal component analysis (PCA) for multi-group datasets, where the same numerical variables are measured across different groups of observations. Existing approaches either ignore group structure entirely by working with global (pooled) data, focus exclusively on local structure (group-wise PCA), or impose restrictive assumptions of common principal components. Our approach respects the multi-group nature of data while improving global comparability of components. We combine group-specific principal components with global ones through an explicit alignment mechanism base"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.21741","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/2607.21741/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.21741","created_at":"2026-07-27T00:20:18.451856+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.21741v1","created_at":"2026-07-27T00:20:18.451856+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.21741","created_at":"2026-07-27T00:20:18.451856+00:00"},{"alias_kind":"pith_short_12","alias_value":"YWFUQRD5K7CI","created_at":"2026-07-27T00:20:18.451856+00:00"},{"alias_kind":"pith_short_16","alias_value":"YWFUQRD5K7CIZ5KJ","created_at":"2026-07-27T00:20:18.451856+00:00"},{"alias_kind":"pith_short_8","alias_value":"YWFUQRD5","created_at":"2026-07-27T00:20:18.451856+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU","json":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU.json","graph_json":"https://pith.science/api/pith-number/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/graph.json","events_json":"https://pith.science/api/pith-number/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/events.json","paper":"https://pith.science/paper/YWFUQRD5"},"agent_actions":{"view_html":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU","download_json":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU.json","view_paper":"https://pith.science/paper/YWFUQRD5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.21741&json=true","fetch_graph":"https://pith.science/api/pith-number/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/graph.json","fetch_events":"https://pith.science/api/pith-number/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/action/storage_attestation","attest_author":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/action/author_attestation","sign_citation":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/action/citation_signature","submit_replication":"https://pith.science/pith/YWFUQRD5K7CIZ5KJ2X2JFE7VPU/action/replication_record"}},"created_at":"2026-07-27T00:20:18.451856+00:00","updated_at":"2026-07-27T00:20:18.451856+00:00"}