{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7WD4PZ7NN2NDKLM6Y4LARD3SWJ","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":"11f2ec85739409319b39a3271eb68ad660730d7e71f1d4c05e7dd349483b340d","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-23T10:02:02Z","title_canon_sha256":"e32e81cb908c2fcd5bbb5b5e0186fad66978c1f7948e8c4bcf432d09b074a755"},"schema_version":"1.0","source":{"id":"1802.08465","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.08465","created_at":"2026-05-18T00:21:40Z"},{"alias_kind":"arxiv_version","alias_value":"1802.08465v2","created_at":"2026-05-18T00:21:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08465","created_at":"2026-05-18T00:21:40Z"},{"alias_kind":"pith_short_12","alias_value":"7WD4PZ7NN2ND","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7WD4PZ7NN2NDKLM6","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7WD4PZ7N","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:4b01b6a80e30eb7002745719cefb1ae5b2e1bb5a1047d9520463f58ee8e3640e","target":"graph","created_at":"2026-05-18T00:21:40Z","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":"High dimensionality, i.e. data having a large number of variables, tends to be a challenge for most machine learning tasks, including classification. A classifier usually builds a model representing how a set of inputs explain the outputs. The larger is the set of inputs and/or outputs, the more complex would be that model. There is a family of classification algorithms, known as lazy learning methods, which does not build a model. One of the best known members of this family is the kNN algorithm. Its strategy relies on searching a set of nearest neighbors, using the input variables as positio","authors_text":"Antonio J. Rivera, Francisco Charte, Francisco J. Pulgar, Mar\\'ia J. del Jesus","cross_cats":["cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-23T10:02:02Z","title":"AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08465","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:c70771521e07e154a65710bf6fef0c9832b06af71dc0ea834a1e65649ee650b9","target":"record","created_at":"2026-05-18T00:21:40Z","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":"11f2ec85739409319b39a3271eb68ad660730d7e71f1d4c05e7dd349483b340d","cross_cats_sorted":["cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-23T10:02:02Z","title_canon_sha256":"e32e81cb908c2fcd5bbb5b5e0186fad66978c1f7948e8c4bcf432d09b074a755"},"schema_version":"1.0","source":{"id":"1802.08465","kind":"arxiv","version":2}},"canonical_sha256":"fd87c7e7ed6e9a352d9ec716088f72b27acdee9b9b6750405b255977b1b34aca","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fd87c7e7ed6e9a352d9ec716088f72b27acdee9b9b6750405b255977b1b34aca","first_computed_at":"2026-05-18T00:21:40.271286Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:40.271286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8mZU+nf6b4suRIeHAiG5q5M2SNEq2Qgdjbq1L4wsJcGjriQneE54vIAUe7ZgUafkkmS41+AEv5ihzbbUMINJCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:40.271927Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.08465","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c70771521e07e154a65710bf6fef0c9832b06af71dc0ea834a1e65649ee650b9","sha256:4b01b6a80e30eb7002745719cefb1ae5b2e1bb5a1047d9520463f58ee8e3640e"],"state_sha256":"aae0308089d60fa9514efc729ae8c4c5599271944247fa3af11fe7dfeac37f3a"}