{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:V2X2HOTHNFYR5MLNNUEHLT2PJ7","short_pith_number":"pith:V2X2HOTH","schema_version":"1.0","canonical_sha256":"aeafa3ba6769711eb16d6d0875cf4f4fda1118e24f51c81adeab5956c37db595","source":{"kind":"arxiv","id":"1812.08954","version":1},"attestation_state":"computed","paper":{"title":"Primal path algorithm for compositional data analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hosik Choi, Jong-June Jeon, Sungho Won, Yongdai Kim","submitted_at":"2018-12-21T05:23:36Z","abstract_excerpt":"Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data. The algorithm is then extended to a classification model with compositional predictors. We als"},"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":"1812.08954","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-21T05:23:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"64b1ee041c4994266930a835cdde307a613d87c9ed40494227ab3115cd9d7216","abstract_canon_sha256":"52bfc4ef876eef1389ba881f08f5ad7970c82dcf58fa4d3502ba702afc410289"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:45.789173Z","signature_b64":"H+PCKYsc3W7uf3XtBFjTbiQnSC1vvWFy5Ij7ofGE73ZAWL5BDSDaehKdROKJlGeNAdNLFTSjfgY79xSmzPICCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aeafa3ba6769711eb16d6d0875cf4f4fda1118e24f51c81adeab5956c37db595","last_reissued_at":"2026-05-17T23:57:45.788582Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:45.788582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Primal path algorithm for compositional data analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hosik Choi, Jong-June Jeon, Sungho Won, Yongdai Kim","submitted_at":"2018-12-21T05:23:36Z","abstract_excerpt":"Compositional data have two unique characteristics compared to typical multivariate data: the observed values are nonnegative and their summand is exactly one. To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used. However, linear constraints incur additional computational time, which becomes severe in high-dimensional cases. As such, we propose an efficient solution path algorithm for a $l_1$ regularized regression with compositional data. The algorithm is then extended to a classification model with compositional predictors. We als"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.08954","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":"1812.08954","created_at":"2026-05-17T23:57:45.788660+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.08954v1","created_at":"2026-05-17T23:57:45.788660+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.08954","created_at":"2026-05-17T23:57:45.788660+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2X2HOTHNFYR","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2X2HOTHNFYR5MLN","created_at":"2026-05-18T12:32:56.356000+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2X2HOTH","created_at":"2026-05-18T12:32:56.356000+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/V2X2HOTHNFYR5MLNNUEHLT2PJ7","json":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7.json","graph_json":"https://pith.science/api/pith-number/V2X2HOTHNFYR5MLNNUEHLT2PJ7/graph.json","events_json":"https://pith.science/api/pith-number/V2X2HOTHNFYR5MLNNUEHLT2PJ7/events.json","paper":"https://pith.science/paper/V2X2HOTH"},"agent_actions":{"view_html":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7","download_json":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7.json","view_paper":"https://pith.science/paper/V2X2HOTH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.08954&json=true","fetch_graph":"https://pith.science/api/pith-number/V2X2HOTHNFYR5MLNNUEHLT2PJ7/graph.json","fetch_events":"https://pith.science/api/pith-number/V2X2HOTHNFYR5MLNNUEHLT2PJ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7/action/storage_attestation","attest_author":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7/action/author_attestation","sign_citation":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7/action/citation_signature","submit_replication":"https://pith.science/pith/V2X2HOTHNFYR5MLNNUEHLT2PJ7/action/replication_record"}},"created_at":"2026-05-17T23:57:45.788660+00:00","updated_at":"2026-05-17T23:57:45.788660+00:00"}