{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:CRBG27N4GCB76R6XGUPD6EDF7Y","short_pith_number":"pith:CRBG27N4","schema_version":"1.0","canonical_sha256":"14426d7dbc3083ff47d7351e3f1065fe21d36e90ae96ed60feb5fdbeb411053f","source":{"kind":"arxiv","id":"1707.09688","version":2},"attestation_state":"computed","paper":{"title":"Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Hiroki Yanagisawa, Masaaki Imaizumi, Ryo Okamoto, Satoshi Hara, Shigeki Takeuchi, Takafumi Ono, Takayuki Katsuki","submitted_at":"2017-07-31T00:53:59Z","abstract_excerpt":"Two-sample feature selection is the problem of finding features that describe a difference between two probability distributions, which is a ubiquitous problem in both scientific and engineering studies. However, existing methods have limited applicability because of their restrictive assumptions on data distributoins or computational difficulty. In this paper, we resolve these difficulties by formulating the problem as a sparsest $k$-subgraph problem. The proposed method is nonparametric and does not assume any specific parametric models on the data distributions. We show that the proposed me"},"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":"1707.09688","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-31T00:53:59Z","cross_cats_sorted":[],"title_canon_sha256":"d09b66ac0c0d58396d913f105b28677814bf6e5f7e946cd78c037a85c235fcf1","abstract_canon_sha256":"224a9141fc83b2c782101ffe50c42d5fc609102bf5395b27745ee2f10e13265e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:06.270547Z","signature_b64":"UdQI3QqUCsI+HBJHzq0Q2LP7n2I504ix4EGu/zZZvLVT5+L9a4KQda80HkR/JvhDaaKnmwy125LRs2mwj95oBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14426d7dbc3083ff47d7351e3f1065fe21d36e90ae96ed60feb5fdbeb411053f","last_reissued_at":"2026-05-18T00:39:06.269902Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:06.269902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Consistent Nonparametric Different-Feature Selection via the Sparsest $k$-Subgraph Problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Hiroki Yanagisawa, Masaaki Imaizumi, Ryo Okamoto, Satoshi Hara, Shigeki Takeuchi, Takafumi Ono, Takayuki Katsuki","submitted_at":"2017-07-31T00:53:59Z","abstract_excerpt":"Two-sample feature selection is the problem of finding features that describe a difference between two probability distributions, which is a ubiquitous problem in both scientific and engineering studies. However, existing methods have limited applicability because of their restrictive assumptions on data distributoins or computational difficulty. In this paper, we resolve these difficulties by formulating the problem as a sparsest $k$-subgraph problem. The proposed method is nonparametric and does not assume any specific parametric models on the data distributions. We show that the proposed me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.09688","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":"1707.09688","created_at":"2026-05-18T00:39:06.270000+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.09688v2","created_at":"2026-05-18T00:39:06.270000+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.09688","created_at":"2026-05-18T00:39:06.270000+00:00"},{"alias_kind":"pith_short_12","alias_value":"CRBG27N4GCB7","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"CRBG27N4GCB76R6X","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"CRBG27N4","created_at":"2026-05-18T12:31:10.602751+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/CRBG27N4GCB76R6XGUPD6EDF7Y","json":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y.json","graph_json":"https://pith.science/api/pith-number/CRBG27N4GCB76R6XGUPD6EDF7Y/graph.json","events_json":"https://pith.science/api/pith-number/CRBG27N4GCB76R6XGUPD6EDF7Y/events.json","paper":"https://pith.science/paper/CRBG27N4"},"agent_actions":{"view_html":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y","download_json":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y.json","view_paper":"https://pith.science/paper/CRBG27N4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.09688&json=true","fetch_graph":"https://pith.science/api/pith-number/CRBG27N4GCB76R6XGUPD6EDF7Y/graph.json","fetch_events":"https://pith.science/api/pith-number/CRBG27N4GCB76R6XGUPD6EDF7Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y/action/storage_attestation","attest_author":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y/action/author_attestation","sign_citation":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y/action/citation_signature","submit_replication":"https://pith.science/pith/CRBG27N4GCB76R6XGUPD6EDF7Y/action/replication_record"}},"created_at":"2026-05-18T00:39:06.270000+00:00","updated_at":"2026-05-18T00:39:06.270000+00:00"}