{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:BNYF6IXEMHARM7XVB4IFBJPJU7","short_pith_number":"pith:BNYF6IXE","schema_version":"1.0","canonical_sha256":"0b705f22e461c1167ef50f1050a5e9a7eb3edf4e90f4cda6e42138918ff484dd","source":{"kind":"arxiv","id":"1506.08002","version":1},"attestation_state":"computed","paper":{"title":"Safe Feature Pruning for Sparse High-Order Interaction Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Ichiro Takeuchi, Kazuya Nakagawa, Koji Tsuda, Masayuki Karasuyama, Shinya Suzumura","submitted_at":"2015-06-26T09:08:26Z","abstract_excerpt":"Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the number of high-order interaction features to be considered would be extremely large unless the number of covariates is sufficiently small. In this paper, we propose a novel efficient algorithm for LASSO-based sparse learning of such high-order interaction models. Our basic strategy for reducing the number of features is to employ the idea of recently proposed safe feature screening (SFS) rule. An SFS rule has a property t"},"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":"1506.08002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-26T09:08:26Z","cross_cats_sorted":[],"title_canon_sha256":"5c995543321b04d35534df564e67e4742864bc5653558de6075f638bcb72d2b9","abstract_canon_sha256":"8ef2606693d6c17d1ab234ac718b02ca330acff4beae5de332ba405a7a5ace71"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:48.308772Z","signature_b64":"98zQk5q6RoWR4etu2xRqt0uRxA5qGwBK507MJnop5qn2Cm3+eyFemm1D/lCl28ENje/GTJ+ud5t9oxW1LIPMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b705f22e461c1167ef50f1050a5e9a7eb3edf4e90f4cda6e42138918ff484dd","last_reissued_at":"2026-05-18T01:37:48.307931Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:48.307931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safe Feature Pruning for Sparse High-Order Interaction Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Ichiro Takeuchi, Kazuya Nakagawa, Koji Tsuda, Masayuki Karasuyama, Shinya Suzumura","submitted_at":"2015-06-26T09:08:26Z","abstract_excerpt":"Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the number of high-order interaction features to be considered would be extremely large unless the number of covariates is sufficiently small. In this paper, we propose a novel efficient algorithm for LASSO-based sparse learning of such high-order interaction models. Our basic strategy for reducing the number of features is to employ the idea of recently proposed safe feature screening (SFS) rule. An SFS rule has a property t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.08002","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":"1506.08002","created_at":"2026-05-18T01:37:48.308078+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.08002v1","created_at":"2026-05-18T01:37:48.308078+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.08002","created_at":"2026-05-18T01:37:48.308078+00:00"},{"alias_kind":"pith_short_12","alias_value":"BNYF6IXEMHAR","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_16","alias_value":"BNYF6IXEMHARM7XV","created_at":"2026-05-18T12:29:14.074870+00:00"},{"alias_kind":"pith_short_8","alias_value":"BNYF6IXE","created_at":"2026-05-18T12:29:14.074870+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/BNYF6IXEMHARM7XVB4IFBJPJU7","json":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7.json","graph_json":"https://pith.science/api/pith-number/BNYF6IXEMHARM7XVB4IFBJPJU7/graph.json","events_json":"https://pith.science/api/pith-number/BNYF6IXEMHARM7XVB4IFBJPJU7/events.json","paper":"https://pith.science/paper/BNYF6IXE"},"agent_actions":{"view_html":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7","download_json":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7.json","view_paper":"https://pith.science/paper/BNYF6IXE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.08002&json=true","fetch_graph":"https://pith.science/api/pith-number/BNYF6IXEMHARM7XVB4IFBJPJU7/graph.json","fetch_events":"https://pith.science/api/pith-number/BNYF6IXEMHARM7XVB4IFBJPJU7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7/action/storage_attestation","attest_author":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7/action/author_attestation","sign_citation":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7/action/citation_signature","submit_replication":"https://pith.science/pith/BNYF6IXEMHARM7XVB4IFBJPJU7/action/replication_record"}},"created_at":"2026-05-18T01:37:48.308078+00:00","updated_at":"2026-05-18T01:37:48.308078+00:00"}