{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:JYI6HPWCTGCREMYB4VU7MGKPL6","short_pith_number":"pith:JYI6HPWC","canonical_record":{"source":{"id":"1201.4714","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-01-23T13:48:33Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2325b2402957ef0db96802dd9750a6295dc9a71f5104fec424a373b6d4583796","abstract_canon_sha256":"ef2c8cc1149fed1af7eac629d691f52c859adb286ea3686c91017274270efb21"},"schema_version":"1.0"},"canonical_sha256":"4e11e3bec29985123301e569f6194f5fb4190b6231e31525dea31025ae24bfcc","source":{"kind":"arxiv","id":"1201.4714","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.4714","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"1201.4714v1","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.4714","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"JYI6HPWCTGCR","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"JYI6HPWCTGCREMYB","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"JYI6HPWC","created_at":"2026-05-18T12:27:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:JYI6HPWCTGCREMYB4VU7MGKPL6","target":"record","payload":{"canonical_record":{"source":{"id":"1201.4714","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-01-23T13:48:33Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2325b2402957ef0db96802dd9750a6295dc9a71f5104fec424a373b6d4583796","abstract_canon_sha256":"ef2c8cc1149fed1af7eac629d691f52c859adb286ea3686c91017274270efb21"},"schema_version":"1.0"},"canonical_sha256":"4e11e3bec29985123301e569f6194f5fb4190b6231e31525dea31025ae24bfcc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:04:05.362767Z","signature_b64":"Jm+Zoc3Be1dIk6OCAVaJlRo+no7biaATKcideay3MhlArb+LFODda4P/zTWA4i5prA6lleNGPfKhKoRN/mvjDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e11e3bec29985123301e569f6194f5fb4190b6231e31525dea31025ae24bfcc","last_reissued_at":"2026-05-18T04:04:05.362353Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:04:05.362353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1201.4714","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":"2026-05-18T04:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GNfGYnh5Z6onzrCKCa2+IW3lA/y1Ez7+VM1KOTF8W4Jkz36c0JZEVF3Y4YioyKIVveT7/eFO5Q16VVCBxhnJBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:54:26.162196Z"},"content_sha256":"659b4ee127c9b1fff1cd33aa5e9ab8930e9cd80780027266a90cae2b8cf1f5b1","schema_version":"1.0","event_id":"sha256:659b4ee127c9b1fff1cd33aa5e9ab8930e9cd80780027266a90cae2b8cf1f5b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:JYI6HPWCTGCREMYB4VU7MGKPL6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A metric learning perspective of SVM: on the relation of SVM and LMNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Adam Woznica, Alexandros Kalousis, Huyen Do, Jun Wang","submitted_at":"2012-01-23T13:48:33Z","abstract_excerpt":"Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metric learning perspective and cast them as a metric learning problem, a view which helps us uncover the relations of the two algorithms. We show that LMNN can be seen as learning a set of local SVM-like models in a quadratic space. Along the way and inspired by the metric-based int"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.4714","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":"2026-05-18T04:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EVRd+c31h9T9icVxnLg63nt8MpbnaZwMcIy5xzy9QmBRTHMP+hqYqbo4MPmvTwFMwhJF1jedpDXqROuENRAzDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:54:26.162908Z"},"content_sha256":"d4c7e375b02fdd5a089bc09168719a2b6b9ef98eea0356c0b37f1547ced147f1","schema_version":"1.0","event_id":"sha256:d4c7e375b02fdd5a089bc09168719a2b6b9ef98eea0356c0b37f1547ced147f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JYI6HPWCTGCREMYB4VU7MGKPL6/bundle.json","state_url":"https://pith.science/pith/JYI6HPWCTGCREMYB4VU7MGKPL6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JYI6HPWCTGCREMYB4VU7MGKPL6/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":"2026-05-26T07:54:26Z","links":{"resolver":"https://pith.science/pith/JYI6HPWCTGCREMYB4VU7MGKPL6","bundle":"https://pith.science/pith/JYI6HPWCTGCREMYB4VU7MGKPL6/bundle.json","state":"https://pith.science/pith/JYI6HPWCTGCREMYB4VU7MGKPL6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JYI6HPWCTGCREMYB4VU7MGKPL6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:JYI6HPWCTGCREMYB4VU7MGKPL6","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":"ef2c8cc1149fed1af7eac629d691f52c859adb286ea3686c91017274270efb21","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-01-23T13:48:33Z","title_canon_sha256":"2325b2402957ef0db96802dd9750a6295dc9a71f5104fec424a373b6d4583796"},"schema_version":"1.0","source":{"id":"1201.4714","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.4714","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"1201.4714v1","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.4714","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"JYI6HPWCTGCR","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"JYI6HPWCTGCREMYB","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"JYI6HPWC","created_at":"2026-05-18T12:27:11Z"}],"graph_snapshots":[{"event_id":"sha256:d4c7e375b02fdd5a089bc09168719a2b6b9ef98eea0356c0b37f1547ced147f1","target":"graph","created_at":"2026-05-18T04:04:05Z","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":"Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metric learning perspective and cast them as a metric learning problem, a view which helps us uncover the relations of the two algorithms. We show that LMNN can be seen as learning a set of local SVM-like models in a quadratic space. Along the way and inspired by the metric-based int","authors_text":"Adam Woznica, Alexandros Kalousis, Huyen Do, Jun Wang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-01-23T13:48:33Z","title":"A metric learning perspective of SVM: on the relation of SVM and LMNN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.4714","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":"sha256:659b4ee127c9b1fff1cd33aa5e9ab8930e9cd80780027266a90cae2b8cf1f5b1","target":"record","created_at":"2026-05-18T04:04:05Z","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":"ef2c8cc1149fed1af7eac629d691f52c859adb286ea3686c91017274270efb21","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-01-23T13:48:33Z","title_canon_sha256":"2325b2402957ef0db96802dd9750a6295dc9a71f5104fec424a373b6d4583796"},"schema_version":"1.0","source":{"id":"1201.4714","kind":"arxiv","version":1}},"canonical_sha256":"4e11e3bec29985123301e569f6194f5fb4190b6231e31525dea31025ae24bfcc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4e11e3bec29985123301e569f6194f5fb4190b6231e31525dea31025ae24bfcc","first_computed_at":"2026-05-18T04:04:05.362353Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:04:05.362353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Jm+Zoc3Be1dIk6OCAVaJlRo+no7biaATKcideay3MhlArb+LFODda4P/zTWA4i5prA6lleNGPfKhKoRN/mvjDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T04:04:05.362767Z","signed_message":"canonical_sha256_bytes"},"source_id":"1201.4714","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:659b4ee127c9b1fff1cd33aa5e9ab8930e9cd80780027266a90cae2b8cf1f5b1","sha256:d4c7e375b02fdd5a089bc09168719a2b6b9ef98eea0356c0b37f1547ced147f1"],"state_sha256":"5f27686da25c6de20261d1654d5021a926f32f05e4f3534862043005214ed68b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x8nSSjqwQUSjIVgF+TmN3oI82wWnXcLirRb2TH0TDaDUH5WFJtdfCOV9RV8XlGKU1nJXcHYEbnULQNFJkAndDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T07:54:26.166509Z","bundle_sha256":"c5aad621b7646f5156b0eff02b227f8c666b640fb6f8b0b27f35067f189b716c"}}