{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:KBO7QUUOTJXQ36Q4S2QVZMIC5R","short_pith_number":"pith:KBO7QUUO","schema_version":"1.0","canonical_sha256":"505df8528e9a6f0dfa1c96a15cb102ec7c3b8c7f89ffde30d995667d5ffd483d","source":{"kind":"arxiv","id":"1808.06220","version":2},"attestation_state":"computed","paper":{"title":"Jointly Deep Multi-View Learning for Clustering Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingqian Lin, Cuihua Li, Xiaodan Liang, Yanyun Qu, Yuan Xie","submitted_at":"2018-08-19T15:17:34Z","abstract_excerpt":"In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignments can be learned simultaneously. Our key idea is that the joint learning strategy can sufficiently exploit clustering-friendly multi-view features and useful multi-view complementary information to improve the clustering performance. How to realize the multi-view fusion in such a joint framework is the primary challenge. To do so, we design two ingenious variants of deep multi-view joint clustering models under the "},"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":"1808.06220","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-19T15:17:34Z","cross_cats_sorted":[],"title_canon_sha256":"5e184a5ea64e892af336221a41661e55eab2a2dac5922f22f3db3535e120c4d2","abstract_canon_sha256":"639da2262b6de8773fa7f30d04f7947f3c7f1b8932a7896c3418f54ab5a86081"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:08.172751Z","signature_b64":"lfO30nWIXgIgtcLsY4Cfwa4v3k4WWIs/Cz19p0mebmzMj5shKuzfc1SCecf8jufHIqE6MFukxCxlxQprvoqqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"505df8528e9a6f0dfa1c96a15cb102ec7c3b8c7f89ffde30d995667d5ffd483d","last_reissued_at":"2026-05-18T00:00:08.172300Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:08.172300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Jointly Deep Multi-View Learning for Clustering Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingqian Lin, Cuihua Li, Xiaodan Liang, Yanyun Qu, Yuan Xie","submitted_at":"2018-08-19T15:17:34Z","abstract_excerpt":"In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignments can be learned simultaneously. Our key idea is that the joint learning strategy can sufficiently exploit clustering-friendly multi-view features and useful multi-view complementary information to improve the clustering performance. How to realize the multi-view fusion in such a joint framework is the primary challenge. To do so, we design two ingenious variants of deep multi-view joint clustering models under the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.06220","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":"1808.06220","created_at":"2026-05-18T00:00:08.172383+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.06220v2","created_at":"2026-05-18T00:00:08.172383+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.06220","created_at":"2026-05-18T00:00:08.172383+00:00"},{"alias_kind":"pith_short_12","alias_value":"KBO7QUUOTJXQ","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"KBO7QUUOTJXQ36Q4","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"KBO7QUUO","created_at":"2026-05-18T12:32:33.847187+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2512.21510","citing_title":"Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R","json":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R.json","graph_json":"https://pith.science/api/pith-number/KBO7QUUOTJXQ36Q4S2QVZMIC5R/graph.json","events_json":"https://pith.science/api/pith-number/KBO7QUUOTJXQ36Q4S2QVZMIC5R/events.json","paper":"https://pith.science/paper/KBO7QUUO"},"agent_actions":{"view_html":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R","download_json":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R.json","view_paper":"https://pith.science/paper/KBO7QUUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.06220&json=true","fetch_graph":"https://pith.science/api/pith-number/KBO7QUUOTJXQ36Q4S2QVZMIC5R/graph.json","fetch_events":"https://pith.science/api/pith-number/KBO7QUUOTJXQ36Q4S2QVZMIC5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R/action/storage_attestation","attest_author":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R/action/author_attestation","sign_citation":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R/action/citation_signature","submit_replication":"https://pith.science/pith/KBO7QUUOTJXQ36Q4S2QVZMIC5R/action/replication_record"}},"created_at":"2026-05-18T00:00:08.172383+00:00","updated_at":"2026-05-18T00:00:08.172383+00:00"}