{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:543THIEKWGJS44HRJT77HLG6E2","short_pith_number":"pith:543THIEK","schema_version":"1.0","canonical_sha256":"ef3733a08ab1932e70f14cfff3acde269b1139b49813ff02cc3d5497caff8364","source":{"kind":"arxiv","id":"1410.3355","version":2},"attestation_state":"computed","paper":{"title":"Resistant Multiple Sparse Canonical Correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Gabriel Chandler, Jacob Coleman, Johanna Hardin, Joseph Replogle","submitted_at":"2014-10-13T15:39:24Z","abstract_excerpt":"Canonical Correlation Analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively.\n  CCA appears to have quite powerful applications to high throughpu"},"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":"1410.3355","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-10-13T15:39:24Z","cross_cats_sorted":[],"title_canon_sha256":"8267b23a7820ceb51647144ef5458a5e085b5a224094e563f3c75c08a67cc9d7","abstract_canon_sha256":"46578883097eaaa2e798456f10342315fa034f232fe1262673c0632a3398b03b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:04.169949Z","signature_b64":"9mvTkPv61ilWQHj8vzhnUPVZxSEqWt1x71KaGyfn5W4vmaJX5zhtsc9gen9ppRbNLulDD7ACv35NnufsRsUqCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef3733a08ab1932e70f14cfff3acde269b1139b49813ff02cc3d5497caff8364","last_reissued_at":"2026-05-18T01:24:04.169165Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:04.169165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Resistant Multiple Sparse Canonical Correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Gabriel Chandler, Jacob Coleman, Johanna Hardin, Joseph Replogle","submitted_at":"2014-10-13T15:39:24Z","abstract_excerpt":"Canonical Correlation Analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively.\n  CCA appears to have quite powerful applications to high throughpu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.3355","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1410.3355","created_at":"2026-05-18T01:24:04.169304+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.3355v2","created_at":"2026-05-18T01:24:04.169304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.3355","created_at":"2026-05-18T01:24:04.169304+00:00"},{"alias_kind":"pith_short_12","alias_value":"543THIEKWGJS","created_at":"2026-05-18T12:28:14.216126+00:00"},{"alias_kind":"pith_short_16","alias_value":"543THIEKWGJS44HR","created_at":"2026-05-18T12:28:14.216126+00:00"},{"alias_kind":"pith_short_8","alias_value":"543THIEK","created_at":"2026-05-18T12:28:14.216126+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/543THIEKWGJS44HRJT77HLG6E2","json":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2.json","graph_json":"https://pith.science/api/pith-number/543THIEKWGJS44HRJT77HLG6E2/graph.json","events_json":"https://pith.science/api/pith-number/543THIEKWGJS44HRJT77HLG6E2/events.json","paper":"https://pith.science/paper/543THIEK"},"agent_actions":{"view_html":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2","download_json":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2.json","view_paper":"https://pith.science/paper/543THIEK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.3355&json=true","fetch_graph":"https://pith.science/api/pith-number/543THIEKWGJS44HRJT77HLG6E2/graph.json","fetch_events":"https://pith.science/api/pith-number/543THIEKWGJS44HRJT77HLG6E2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2/action/storage_attestation","attest_author":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2/action/author_attestation","sign_citation":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2/action/citation_signature","submit_replication":"https://pith.science/pith/543THIEKWGJS44HRJT77HLG6E2/action/replication_record"}},"created_at":"2026-05-18T01:24:04.169304+00:00","updated_at":"2026-05-18T01:24:04.169304+00:00"}