{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:TIRO63EVYNPSGMJXWEVA66C6MG","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":"217cee59734b3d6c9131e4f776799e9dbbe5aeb737fbe21e41d02a6d6333e4f6","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-02T05:30:44Z","title_canon_sha256":"9a8f80b42d25af4d1de56b1480aeb711eca4ef10ae5b6b64457c71c7257e7f91"},"schema_version":"1.0","source":{"id":"1502.00363","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.00363","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"arxiv_version","alias_value":"1502.00363v1","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.00363","created_at":"2026-05-18T02:28:06Z"},{"alias_kind":"pith_short_12","alias_value":"TIRO63EVYNPS","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_16","alias_value":"TIRO63EVYNPSGMJX","created_at":"2026-05-18T12:29:42Z"},{"alias_kind":"pith_short_8","alias_value":"TIRO63EV","created_at":"2026-05-18T12:29:42Z"}],"graph_snapshots":[{"event_id":"sha256:e550e5f8b6d2a67db7dcf78df88df5c481055ceccc9ada677234e78f3dc4587a","target":"graph","created_at":"2026-05-18T02:28:06Z","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":"Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while many existing metric learning algorithms become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem, and solve it by iterated training of support vector machines (SVM). The new formulation is easy to implement, efficient in training, and tractable for ","authors_text":"David Zhang, Deyu Meng, Faqiang Wang, Lei Zhang, Liang Lin, Wangmeng Zuo, Yuchi Huang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-02T05:30:44Z","title":"Iterated Support Vector Machines for Distance Metric Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.00363","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:f1f5100d3ae22d91d243e1841ddc31dd525468e2a613e4cb1a623e0e3a69c6b5","target":"record","created_at":"2026-05-18T02:28:06Z","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":"217cee59734b3d6c9131e4f776799e9dbbe5aeb737fbe21e41d02a6d6333e4f6","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-02-02T05:30:44Z","title_canon_sha256":"9a8f80b42d25af4d1de56b1480aeb711eca4ef10ae5b6b64457c71c7257e7f91"},"schema_version":"1.0","source":{"id":"1502.00363","kind":"arxiv","version":1}},"canonical_sha256":"9a22ef6c95c35f233137b12a0f785e619d67c70cc56170fd53bd032d68c0ae2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a22ef6c95c35f233137b12a0f785e619d67c70cc56170fd53bd032d68c0ae2c","first_computed_at":"2026-05-18T02:28:06.163115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:28:06.163115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aEFYIrS6JqMUb3t9QB50Jzg3m54iMyQBHKXjimPMa0sCcRFq0j7YXDV5pCw/bS/AH+FqhtHhEcnz5IEtor24BA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:28:06.163778Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.00363","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f1f5100d3ae22d91d243e1841ddc31dd525468e2a613e4cb1a623e0e3a69c6b5","sha256:e550e5f8b6d2a67db7dcf78df88df5c481055ceccc9ada677234e78f3dc4587a"],"state_sha256":"56caafff45cde520aab4534a7901ebf637474f932d23564f4139b9b5d5380ba9"}