{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XHHM6ON2HXGHQUJHKEACPDZFHW","short_pith_number":"pith:XHHM6ON2","schema_version":"1.0","canonical_sha256":"b9cecf39ba3dcc7851275100278f253d916ee082616e3c09c5a9d6083e791ac0","source":{"kind":"arxiv","id":"1811.01557","version":2},"attestation_state":"computed","paper":{"title":"Representation Learning by Reconstructing Neighborhoods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abdullah Mueen, Chin-Chia Michael Yeh, Eamonn Keogh, Evangelos E. Papalexakis, Yan Zhu","submitted_at":"2018-11-05T08:56:21Z","abstract_excerpt":"Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning. In this work, we propose a novel unsupervised representation learning framework called neighbor-encoder, in which domain knowledge can be easily incorporated into the learning process without modifying the general encoder-decoder architecture of the classic autoencoder.In contrast to autoencoder"},"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":"1811.01557","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-05T08:56:21Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"0c51fa41043eefe7e46e6c5824c1cb5e9d285d921226f535a8587e1bd624ddf8","abstract_canon_sha256":"e067cacefbd15fd4e33b120b1416b7e59dc04250a0a446390cc47047eb99ed18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:25.173423Z","signature_b64":"stksiFBviihOEdEkaQuKZ796A6e8/9KffdwWImtcV59cfrT2bIT/vntiTA5y52Y6U+YF7yEskrmFFLmxAtHjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9cecf39ba3dcc7851275100278f253d916ee082616e3c09c5a9d6083e791ac0","last_reissued_at":"2026-05-18T00:01:25.172929Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:25.172929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Representation Learning by Reconstructing Neighborhoods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Abdullah Mueen, Chin-Chia Michael Yeh, Eamonn Keogh, Evangelos E. Papalexakis, Yan Zhu","submitted_at":"2018-11-05T08:56:21Z","abstract_excerpt":"Since its introduction, unsupervised representation learning has attracted a lot of attention from the research community, as it is demonstrated to be highly effective and easy-to-apply in tasks such as dimension reduction, clustering, visualization, information retrieval, and semi-supervised learning. In this work, we propose a novel unsupervised representation learning framework called neighbor-encoder, in which domain knowledge can be easily incorporated into the learning process without modifying the general encoder-decoder architecture of the classic autoencoder.In contrast to autoencoder"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.01557","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":"1811.01557","created_at":"2026-05-18T00:01:25.173003+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.01557v2","created_at":"2026-05-18T00:01:25.173003+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.01557","created_at":"2026-05-18T00:01:25.173003+00:00"},{"alias_kind":"pith_short_12","alias_value":"XHHM6ON2HXGH","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"XHHM6ON2HXGHQUJH","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"XHHM6ON2","created_at":"2026-05-18T12:33:01.666342+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/XHHM6ON2HXGHQUJHKEACPDZFHW","json":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW.json","graph_json":"https://pith.science/api/pith-number/XHHM6ON2HXGHQUJHKEACPDZFHW/graph.json","events_json":"https://pith.science/api/pith-number/XHHM6ON2HXGHQUJHKEACPDZFHW/events.json","paper":"https://pith.science/paper/XHHM6ON2"},"agent_actions":{"view_html":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW","download_json":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW.json","view_paper":"https://pith.science/paper/XHHM6ON2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.01557&json=true","fetch_graph":"https://pith.science/api/pith-number/XHHM6ON2HXGHQUJHKEACPDZFHW/graph.json","fetch_events":"https://pith.science/api/pith-number/XHHM6ON2HXGHQUJHKEACPDZFHW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW/action/storage_attestation","attest_author":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW/action/author_attestation","sign_citation":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW/action/citation_signature","submit_replication":"https://pith.science/pith/XHHM6ON2HXGHQUJHKEACPDZFHW/action/replication_record"}},"created_at":"2026-05-18T00:01:25.173003+00:00","updated_at":"2026-05-18T00:01:25.173003+00:00"}