{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:6GK2HE7EQJSLS7JLLAW233TMMC","short_pith_number":"pith:6GK2HE7E","canonical_record":{"source":{"id":"1103.1013","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2011-03-05T07:10:41Z","cross_cats_sorted":[],"title_canon_sha256":"bfa9bacbc8213647dc5e3d9325be6db588dc83d26c975d445ffaaf83b8150293","abstract_canon_sha256":"8dd6b39966ce16064a63497605f7ef6b8ba7a82036629b72eee703ce681d4904"},"schema_version":"1.0"},"canonical_sha256":"f195a393e48264b97d2b582dadee6c60be43641905a1821725cd1243eb77e910","source":{"kind":"arxiv","id":"1103.1013","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1103.1013","created_at":"2026-05-18T02:22:30Z"},{"alias_kind":"arxiv_version","alias_value":"1103.1013v2","created_at":"2026-05-18T02:22:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1103.1013","created_at":"2026-05-18T02:22:30Z"},{"alias_kind":"pith_short_12","alias_value":"6GK2HE7EQJSL","created_at":"2026-05-18T12:26:22Z"},{"alias_kind":"pith_short_16","alias_value":"6GK2HE7EQJSLS7JL","created_at":"2026-05-18T12:26:22Z"},{"alias_kind":"pith_short_8","alias_value":"6GK2HE7E","created_at":"2026-05-18T12:26:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:6GK2HE7EQJSLS7JLLAW233TMMC","target":"record","payload":{"canonical_record":{"source":{"id":"1103.1013","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2011-03-05T07:10:41Z","cross_cats_sorted":[],"title_canon_sha256":"bfa9bacbc8213647dc5e3d9325be6db588dc83d26c975d445ffaaf83b8150293","abstract_canon_sha256":"8dd6b39966ce16064a63497605f7ef6b8ba7a82036629b72eee703ce681d4904"},"schema_version":"1.0"},"canonical_sha256":"f195a393e48264b97d2b582dadee6c60be43641905a1821725cd1243eb77e910","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:22:30.867267Z","signature_b64":"sF7N8GpfEOtqwUnWwFGw1l1OMIlzYWzi8WmAD2jrn6FOwFbDV7MRvIwfsXCcHMWQv5akdSatnOGg1BPrmuYdAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f195a393e48264b97d2b582dadee6c60be43641905a1821725cd1243eb77e910","last_reissued_at":"2026-05-18T02:22:30.866833Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:22:30.866833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1103.1013","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:22:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yds7BKm/xSOWpM2vulOiCQ+RZUysJYej4gtMpW+oLjbLWMu1SKhgWLgk7/UixRoHVlY5nO1KXAyiKNgkMGseAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:14:23.695021Z"},"content_sha256":"587081e54f3760d21f3beeb333c9ea1deb5cadec24b45612f31ff2abcfe76e83","schema_version":"1.0","event_id":"sha256:587081e54f3760d21f3beeb333c9ea1deb5cadec24b45612f31ff2abcfe76e83"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:6GK2HE7EQJSLS7JLLAW233TMMC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Feature Selection Method for Multivariate Performance Measures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Ivor W. Tsang, Qi Mao","submitted_at":"2011-03-05T07:10:41Z","abstract_excerpt":"Feature selection with specific multivariate performance measures is the key to the success of many applications, such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimension"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1103.1013","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:22:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MI5uHKpSDG7iRQWnBFFHKpfsbtOoQOvSbzYIKcD/gvF9//vhxhb25yhzJUuFSr0p1piDkEVnb9RXMw3HMBJKCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:14:23.695375Z"},"content_sha256":"50e0039c8bd4db243a440760b51aa6990b96c8ec20c5f8547ca5acd4502ff630","schema_version":"1.0","event_id":"sha256:50e0039c8bd4db243a440760b51aa6990b96c8ec20c5f8547ca5acd4502ff630"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6GK2HE7EQJSLS7JLLAW233TMMC/bundle.json","state_url":"https://pith.science/pith/6GK2HE7EQJSLS7JLLAW233TMMC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6GK2HE7EQJSLS7JLLAW233TMMC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T23:14:23Z","links":{"resolver":"https://pith.science/pith/6GK2HE7EQJSLS7JLLAW233TMMC","bundle":"https://pith.science/pith/6GK2HE7EQJSLS7JLLAW233TMMC/bundle.json","state":"https://pith.science/pith/6GK2HE7EQJSLS7JLLAW233TMMC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6GK2HE7EQJSLS7JLLAW233TMMC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:6GK2HE7EQJSLS7JLLAW233TMMC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8dd6b39966ce16064a63497605f7ef6b8ba7a82036629b72eee703ce681d4904","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2011-03-05T07:10:41Z","title_canon_sha256":"bfa9bacbc8213647dc5e3d9325be6db588dc83d26c975d445ffaaf83b8150293"},"schema_version":"1.0","source":{"id":"1103.1013","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1103.1013","created_at":"2026-05-18T02:22:30Z"},{"alias_kind":"arxiv_version","alias_value":"1103.1013v2","created_at":"2026-05-18T02:22:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1103.1013","created_at":"2026-05-18T02:22:30Z"},{"alias_kind":"pith_short_12","alias_value":"6GK2HE7EQJSL","created_at":"2026-05-18T12:26:22Z"},{"alias_kind":"pith_short_16","alias_value":"6GK2HE7EQJSLS7JL","created_at":"2026-05-18T12:26:22Z"},{"alias_kind":"pith_short_8","alias_value":"6GK2HE7E","created_at":"2026-05-18T12:26:22Z"}],"graph_snapshots":[{"event_id":"sha256:50e0039c8bd4db243a440760b51aa6990b96c8ec20c5f8547ca5acd4502ff630","target":"graph","created_at":"2026-05-18T02:22:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Feature selection with specific multivariate performance measures is the key to the success of many applications, such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a unified feature selection framework for general loss functions. In particular, we study the novel feature selection paradigm by optimizing multivariate performance measures. The resultant formulation is a challenging problem for high-dimension","authors_text":"Ivor W. Tsang, Qi Mao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2011-03-05T07:10:41Z","title":"A Feature Selection Method for Multivariate Performance Measures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1103.1013","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:587081e54f3760d21f3beeb333c9ea1deb5cadec24b45612f31ff2abcfe76e83","target":"record","created_at":"2026-05-18T02:22:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8dd6b39966ce16064a63497605f7ef6b8ba7a82036629b72eee703ce681d4904","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2011-03-05T07:10:41Z","title_canon_sha256":"bfa9bacbc8213647dc5e3d9325be6db588dc83d26c975d445ffaaf83b8150293"},"schema_version":"1.0","source":{"id":"1103.1013","kind":"arxiv","version":2}},"canonical_sha256":"f195a393e48264b97d2b582dadee6c60be43641905a1821725cd1243eb77e910","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f195a393e48264b97d2b582dadee6c60be43641905a1821725cd1243eb77e910","first_computed_at":"2026-05-18T02:22:30.866833Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:22:30.866833Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sF7N8GpfEOtqwUnWwFGw1l1OMIlzYWzi8WmAD2jrn6FOwFbDV7MRvIwfsXCcHMWQv5akdSatnOGg1BPrmuYdAA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:22:30.867267Z","signed_message":"canonical_sha256_bytes"},"source_id":"1103.1013","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:587081e54f3760d21f3beeb333c9ea1deb5cadec24b45612f31ff2abcfe76e83","sha256:50e0039c8bd4db243a440760b51aa6990b96c8ec20c5f8547ca5acd4502ff630"],"state_sha256":"953284fbb5051c2f65d74d770377d477d9e65a243fde9defdee6e037176e1e81"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VowzMF4y10OO+oZu9KURlHeHCan7gxhsCS/1P6z9MhSBkqSPlT76Kou/yoV7dVjf3URGqnTaK1fwm3lMIbIECQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:14:23.697567Z","bundle_sha256":"bef484fdbdd63bb4de6c5564bab6e8223806ddbd4936d477b0327ae0626e2af7"}}