{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:5PHNTHRLRS7PTI2HIEPSNGDG3B","short_pith_number":"pith:5PHNTHRL","canonical_record":{"source":{"id":"1409.5330","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-09-18T15:09:47Z","cross_cats_sorted":[],"title_canon_sha256":"8b87f0f530c8d33a041201610669bb21cac47df49dc05feb650f4537dd534655","abstract_canon_sha256":"91397a725e3bc67cce12b26741813fe301852f7098752a0b5e4e6220b6b17117"},"schema_version":"1.0"},"canonical_sha256":"ebced99e2b8cbef9a347411f269866d84438682c53158f12289ceba344b26966","source":{"kind":"arxiv","id":"1409.5330","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.5330","created_at":"2026-05-18T02:42:29Z"},{"alias_kind":"arxiv_version","alias_value":"1409.5330v1","created_at":"2026-05-18T02:42:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.5330","created_at":"2026-05-18T02:42:29Z"},{"alias_kind":"pith_short_12","alias_value":"5PHNTHRLRS7P","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_16","alias_value":"5PHNTHRLRS7PTI2H","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_8","alias_value":"5PHNTHRL","created_at":"2026-05-18T12:28:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:5PHNTHRLRS7PTI2HIEPSNGDG3B","target":"record","payload":{"canonical_record":{"source":{"id":"1409.5330","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-09-18T15:09:47Z","cross_cats_sorted":[],"title_canon_sha256":"8b87f0f530c8d33a041201610669bb21cac47df49dc05feb650f4537dd534655","abstract_canon_sha256":"91397a725e3bc67cce12b26741813fe301852f7098752a0b5e4e6220b6b17117"},"schema_version":"1.0"},"canonical_sha256":"ebced99e2b8cbef9a347411f269866d84438682c53158f12289ceba344b26966","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:42:29.835581Z","signature_b64":"GJ2Zmo3vS9e/d3kT1p2Dk1byreYuCY5Yjd21WvbaPzmClhK+TN7M2T//Bkz7y7MiJCC/MJemh1aqr2kjFK0wBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebced99e2b8cbef9a347411f269866d84438682c53158f12289ceba344b26966","last_reissued_at":"2026-05-18T02:42:29.834937Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:42:29.834937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.5330","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:42:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mSKngp2gUbdWuPfP4/eGevekAp1KsoHir0/TxT0wZ95Q/gwz2pwfyK8MfGc78I8xuhB561C7ItIsMujHCWRcAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T18:53:05.428976Z"},"content_sha256":"c7ab77725ff6be27025e6be29e08e0b6f78a0ae89a4ad2a44d3b2e7a907186bf","schema_version":"1.0","event_id":"sha256:c7ab77725ff6be27025e6be29e08e0b6f78a0ae89a4ad2a44d3b2e7a907186bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:5PHNTHRLRS7PTI2HIEPSNGDG3B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning and approximation capability of orthogonal super greedy algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jian Fang, Shaobo Lin, Zongben Xu","submitted_at":"2014-09-18T15:09:47Z","abstract_excerpt":"We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning. OSGA is concerned with selecting more than one atoms in each iteration step, which, of course, greatly reduces the computational burden when compared with the conventional orthogonal greedy algorithm (OGA). We prove that even for function classes that are not the convex hull of the dictionary, OSGA does not degrade the approximation capability of OGA provided the dictionary is incoherent. Based on this, we deduce a tight generalization error bound for OSGA learning"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.5330","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:42:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pUeCPcMLubhuYLBEiG9V3rVhOa12Fhe9fuSaqKkl/sZAKZQST9FZY9Xr4MlyfUQh+OgtLIeyLHnAbU0OPyLzBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T18:53:05.429305Z"},"content_sha256":"730c0a399273be9b314444c3bbe1e346a7f5044dde0d8e0f712149e700741bf3","schema_version":"1.0","event_id":"sha256:730c0a399273be9b314444c3bbe1e346a7f5044dde0d8e0f712149e700741bf3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5PHNTHRLRS7PTI2HIEPSNGDG3B/bundle.json","state_url":"https://pith.science/pith/5PHNTHRLRS7PTI2HIEPSNGDG3B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5PHNTHRLRS7PTI2HIEPSNGDG3B/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":"2026-06-25T18:53:05Z","links":{"resolver":"https://pith.science/pith/5PHNTHRLRS7PTI2HIEPSNGDG3B","bundle":"https://pith.science/pith/5PHNTHRLRS7PTI2HIEPSNGDG3B/bundle.json","state":"https://pith.science/pith/5PHNTHRLRS7PTI2HIEPSNGDG3B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5PHNTHRLRS7PTI2HIEPSNGDG3B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:5PHNTHRLRS7PTI2HIEPSNGDG3B","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":"91397a725e3bc67cce12b26741813fe301852f7098752a0b5e4e6220b6b17117","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-09-18T15:09:47Z","title_canon_sha256":"8b87f0f530c8d33a041201610669bb21cac47df49dc05feb650f4537dd534655"},"schema_version":"1.0","source":{"id":"1409.5330","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.5330","created_at":"2026-05-18T02:42:29Z"},{"alias_kind":"arxiv_version","alias_value":"1409.5330v1","created_at":"2026-05-18T02:42:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.5330","created_at":"2026-05-18T02:42:29Z"},{"alias_kind":"pith_short_12","alias_value":"5PHNTHRLRS7P","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_16","alias_value":"5PHNTHRLRS7PTI2H","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_8","alias_value":"5PHNTHRL","created_at":"2026-05-18T12:28:14Z"}],"graph_snapshots":[{"event_id":"sha256:730c0a399273be9b314444c3bbe1e346a7f5044dde0d8e0f712149e700741bf3","target":"graph","created_at":"2026-05-18T02:42:29Z","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":"We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning. OSGA is concerned with selecting more than one atoms in each iteration step, which, of course, greatly reduces the computational burden when compared with the conventional orthogonal greedy algorithm (OGA). We prove that even for function classes that are not the convex hull of the dictionary, OSGA does not degrade the approximation capability of OGA provided the dictionary is incoherent. Based on this, we deduce a tight generalization error bound for OSGA learning","authors_text":"Jian Fang, Shaobo Lin, Zongben Xu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-09-18T15:09:47Z","title":"Learning and approximation capability of orthogonal super greedy algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.5330","kind":"arxiv","version":1},"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:c7ab77725ff6be27025e6be29e08e0b6f78a0ae89a4ad2a44d3b2e7a907186bf","target":"record","created_at":"2026-05-18T02:42:29Z","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":"91397a725e3bc67cce12b26741813fe301852f7098752a0b5e4e6220b6b17117","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-09-18T15:09:47Z","title_canon_sha256":"8b87f0f530c8d33a041201610669bb21cac47df49dc05feb650f4537dd534655"},"schema_version":"1.0","source":{"id":"1409.5330","kind":"arxiv","version":1}},"canonical_sha256":"ebced99e2b8cbef9a347411f269866d84438682c53158f12289ceba344b26966","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebced99e2b8cbef9a347411f269866d84438682c53158f12289ceba344b26966","first_computed_at":"2026-05-18T02:42:29.834937Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:42:29.834937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GJ2Zmo3vS9e/d3kT1p2Dk1byreYuCY5Yjd21WvbaPzmClhK+TN7M2T//Bkz7y7MiJCC/MJemh1aqr2kjFK0wBg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:42:29.835581Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.5330","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c7ab77725ff6be27025e6be29e08e0b6f78a0ae89a4ad2a44d3b2e7a907186bf","sha256:730c0a399273be9b314444c3bbe1e346a7f5044dde0d8e0f712149e700741bf3"],"state_sha256":"b65b018b914de75c99f33f7ff0a550817d2acf6908b14f35f565c754b365721d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M9LVS/g9Khf6gtr/ialL+LhngsUuqcS7F/Kvj7B4uqeOfZ8Gl3d9HHsTVrjRSvq5Fc8mP6wCNHWdq2/hJeI/Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T18:53:05.431184Z","bundle_sha256":"ff20c88574b984923ad990136945afb41f08352a032db90e7721b3004d1bacb3"}}