{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:YLW5LXNOGM7EXZ4CAGWVFIHOIG","short_pith_number":"pith:YLW5LXNO","schema_version":"1.0","canonical_sha256":"c2edd5ddae333e4be78201ad52a0ee41875fd090a5ccf602eb2841fb808072b0","source":{"kind":"arxiv","id":"1103.4998","version":1},"attestation_state":"computed","paper":{"title":"Sufficient Component Analysis for Supervised Dimension Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Gang Niu, Jun Takagi, Makoto Yamada, Masashi Sugiyama","submitted_at":"2011-03-25T15:35:16Z","abstract_excerpt":"The purpose of sufficient dimension reduction (SDR) is to find the low-dimensional subspace of input features that is sufficient for predicting output values. In this paper, we propose a novel distribution-free SDR method called sufficient component analysis (SCA), which is computationally more efficient than existing methods. In our method, a solution is computed by iteratively performing dependence estimation and maximization: Dependence estimation is analytically carried out by recently-proposed least-squares mutual information (LSMI), and dependence maximization is also analytically carrie"},"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":"1103.4998","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-03-25T15:35:16Z","cross_cats_sorted":[],"title_canon_sha256":"d5bd7bc9e09b497dbe849762eb14dfd0ac83149c1f730bb9c090a93cec183ca7","abstract_canon_sha256":"ff06e3913d809b5d28f7c68816eccff4798cde0b609c37822dec715a94ae5f79"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:25:48.241963Z","signature_b64":"5O9DRX6vhaib9LwdOfqV8KiFM8zE8oqaZXgKfU+RX1Uf/8mq5n1SUHIMQYitXt882OMl0LqaOnekRhZB/R68AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2edd5ddae333e4be78201ad52a0ee41875fd090a5ccf602eb2841fb808072b0","last_reissued_at":"2026-05-18T04:25:48.241440Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:25:48.241440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sufficient Component Analysis for Supervised Dimension Reduction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Gang Niu, Jun Takagi, Makoto Yamada, Masashi Sugiyama","submitted_at":"2011-03-25T15:35:16Z","abstract_excerpt":"The purpose of sufficient dimension reduction (SDR) is to find the low-dimensional subspace of input features that is sufficient for predicting output values. In this paper, we propose a novel distribution-free SDR method called sufficient component analysis (SCA), which is computationally more efficient than existing methods. In our method, a solution is computed by iteratively performing dependence estimation and maximization: Dependence estimation is analytically carried out by recently-proposed least-squares mutual information (LSMI), and dependence maximization is also analytically carrie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1103.4998","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1103.4998","created_at":"2026-05-18T04:25:48.241540+00:00"},{"alias_kind":"arxiv_version","alias_value":"1103.4998v1","created_at":"2026-05-18T04:25:48.241540+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1103.4998","created_at":"2026-05-18T04:25:48.241540+00:00"},{"alias_kind":"pith_short_12","alias_value":"YLW5LXNOGM7E","created_at":"2026-05-18T12:26:47.523578+00:00"},{"alias_kind":"pith_short_16","alias_value":"YLW5LXNOGM7EXZ4C","created_at":"2026-05-18T12:26:47.523578+00:00"},{"alias_kind":"pith_short_8","alias_value":"YLW5LXNO","created_at":"2026-05-18T12:26:47.523578+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/YLW5LXNOGM7EXZ4CAGWVFIHOIG","json":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG.json","graph_json":"https://pith.science/api/pith-number/YLW5LXNOGM7EXZ4CAGWVFIHOIG/graph.json","events_json":"https://pith.science/api/pith-number/YLW5LXNOGM7EXZ4CAGWVFIHOIG/events.json","paper":"https://pith.science/paper/YLW5LXNO"},"agent_actions":{"view_html":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG","download_json":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG.json","view_paper":"https://pith.science/paper/YLW5LXNO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1103.4998&json=true","fetch_graph":"https://pith.science/api/pith-number/YLW5LXNOGM7EXZ4CAGWVFIHOIG/graph.json","fetch_events":"https://pith.science/api/pith-number/YLW5LXNOGM7EXZ4CAGWVFIHOIG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG/action/storage_attestation","attest_author":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG/action/author_attestation","sign_citation":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG/action/citation_signature","submit_replication":"https://pith.science/pith/YLW5LXNOGM7EXZ4CAGWVFIHOIG/action/replication_record"}},"created_at":"2026-05-18T04:25:48.241540+00:00","updated_at":"2026-05-18T04:25:48.241540+00:00"}