{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RBBYXKGXC2UY2W45JE4DCLDPNP","short_pith_number":"pith:RBBYXKGX","schema_version":"1.0","canonical_sha256":"88438ba8d716a98d5b9d4938312c6f6beac6187e9cbd0a18fc6530eb64effb0e","source":{"kind":"arxiv","id":"1610.05710","version":2},"attestation_state":"computed","paper":{"title":"Feasibility Based Large Margin Nearest Neighbor Metric Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DS","authors_text":"Babak Hosseini, Barbara Hammer","submitted_at":"2016-10-18T17:06:26Z","abstract_excerpt":"Large margin nearest neighbor (LMNN) is a metric learner which optimizes the performance of the popular $k$NN classifier. However, its resulting metric relies on pre-selected target neighbors. In this paper, we address the feasibility of LMNN's optimization constraints regarding these target points, and introduce a mathematical measure to evaluate the size of the feasible region of the optimization problem. We enhance the optimization framework of LMNN by a weighting scheme which prefers data triplets which yield a larger feasible region. This increases the chances to obtain a good metric as t"},"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":"1610.05710","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2016-10-18T17:06:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5f81eb6f2210a65f5366dee3d583669468dd7d92a48b0458f5b9066a76ddaf12","abstract_canon_sha256":"b894980d980ae6fa59dfc9bd1513a67582d9103c34dbc198e0e1132761588c13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:02.079297Z","signature_b64":"ispXGYEfJdlYq95ESy/nX707opMPT0nEtvmKTbdFovo+PPR7IS8Op4vFd6XA4wI6Yn+c0BvzxSMpBOJAhjgpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88438ba8d716a98d5b9d4938312c6f6beac6187e9cbd0a18fc6530eb64effb0e","last_reissued_at":"2026-05-18T00:17:02.078746Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:02.078746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feasibility Based Large Margin Nearest Neighbor Metric Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.DS","authors_text":"Babak Hosseini, Barbara Hammer","submitted_at":"2016-10-18T17:06:26Z","abstract_excerpt":"Large margin nearest neighbor (LMNN) is a metric learner which optimizes the performance of the popular $k$NN classifier. However, its resulting metric relies on pre-selected target neighbors. In this paper, we address the feasibility of LMNN's optimization constraints regarding these target points, and introduce a mathematical measure to evaluate the size of the feasible region of the optimization problem. We enhance the optimization framework of LMNN by a weighting scheme which prefers data triplets which yield a larger feasible region. This increases the chances to obtain a good metric as t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.05710","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":"1610.05710","created_at":"2026-05-18T00:17:02.078840+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.05710v2","created_at":"2026-05-18T00:17:02.078840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.05710","created_at":"2026-05-18T00:17:02.078840+00:00"},{"alias_kind":"pith_short_12","alias_value":"RBBYXKGXC2UY","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RBBYXKGXC2UY2W45","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RBBYXKGX","created_at":"2026-05-18T12:30:41.710351+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/RBBYXKGXC2UY2W45JE4DCLDPNP","json":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP.json","graph_json":"https://pith.science/api/pith-number/RBBYXKGXC2UY2W45JE4DCLDPNP/graph.json","events_json":"https://pith.science/api/pith-number/RBBYXKGXC2UY2W45JE4DCLDPNP/events.json","paper":"https://pith.science/paper/RBBYXKGX"},"agent_actions":{"view_html":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP","download_json":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP.json","view_paper":"https://pith.science/paper/RBBYXKGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.05710&json=true","fetch_graph":"https://pith.science/api/pith-number/RBBYXKGXC2UY2W45JE4DCLDPNP/graph.json","fetch_events":"https://pith.science/api/pith-number/RBBYXKGXC2UY2W45JE4DCLDPNP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP/action/storage_attestation","attest_author":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP/action/author_attestation","sign_citation":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP/action/citation_signature","submit_replication":"https://pith.science/pith/RBBYXKGXC2UY2W45JE4DCLDPNP/action/replication_record"}},"created_at":"2026-05-18T00:17:02.078840+00:00","updated_at":"2026-05-18T00:17:02.078840+00:00"}