{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZAXKVEFJF3S4R67VK5XAPBMYAB","short_pith_number":"pith:ZAXKVEFJ","schema_version":"1.0","canonical_sha256":"c82eaa90a92ee5c8fbf5576e078598007158daa875999da5990e5c1a60fdb0cd","source":{"kind":"arxiv","id":"1905.05053","version":1},"attestation_state":"computed","paper":{"title":"Multi-View Multiple Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Carlotta Domeniconi, Guoxian Yu, Jun Wang, Shixing Yao, Xiangliang Zhang","submitted_at":"2019-05-13T14:20:44Z","abstract_excerpt":"Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are designed for single-view data. We assume that the individuality and commonality of multi-view data can be leveraged to generate high-quality and diverse clusterings. To this end, we propose a novel multi-view multiple clustering (MVMC) algorithm. MVMC first adapts multi-view self-representation learning to explore the individuality encoding matrices and the shared commonality matrix of multi-view data. It additionally"},"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":"1905.05053","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-13T14:20:44Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"63084f7ec89fbd8812b7420769fc053f535d8bdea46e08335c2a0b6de01c58e0","abstract_canon_sha256":"61f372f7e3098adc3cc7315adc46c502517c2f08fb48fbeb71dd432d82bfc3ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:08.672160Z","signature_b64":"hXahFhWKrn9/MH/yThaCwxEVgVpzuwCipPlCYwOjUe2rYIgKMD6UdMSzPR7LAnoL3YqcHTG12+yR+PaIMNEYAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c82eaa90a92ee5c8fbf5576e078598007158daa875999da5990e5c1a60fdb0cd","last_reissued_at":"2026-05-17T23:46:08.671628Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:08.671628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-View Multiple Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Carlotta Domeniconi, Guoxian Yu, Jun Wang, Shixing Yao, Xiangliang Zhang","submitted_at":"2019-05-13T14:20:44Z","abstract_excerpt":"Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are designed for single-view data. We assume that the individuality and commonality of multi-view data can be leveraged to generate high-quality and diverse clusterings. To this end, we propose a novel multi-view multiple clustering (MVMC) algorithm. MVMC first adapts multi-view self-representation learning to explore the individuality encoding matrices and the shared commonality matrix of multi-view data. It additionally"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05053","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1905.05053","created_at":"2026-05-17T23:46:08.671710+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.05053v1","created_at":"2026-05-17T23:46:08.671710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05053","created_at":"2026-05-17T23:46:08.671710+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZAXKVEFJF3S4","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZAXKVEFJF3S4R67V","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZAXKVEFJ","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2510.18326","citing_title":"Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB","json":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB.json","graph_json":"https://pith.science/api/pith-number/ZAXKVEFJF3S4R67VK5XAPBMYAB/graph.json","events_json":"https://pith.science/api/pith-number/ZAXKVEFJF3S4R67VK5XAPBMYAB/events.json","paper":"https://pith.science/paper/ZAXKVEFJ"},"agent_actions":{"view_html":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB","download_json":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB.json","view_paper":"https://pith.science/paper/ZAXKVEFJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.05053&json=true","fetch_graph":"https://pith.science/api/pith-number/ZAXKVEFJF3S4R67VK5XAPBMYAB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZAXKVEFJF3S4R67VK5XAPBMYAB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB/action/storage_attestation","attest_author":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB/action/author_attestation","sign_citation":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB/action/citation_signature","submit_replication":"https://pith.science/pith/ZAXKVEFJF3S4R67VK5XAPBMYAB/action/replication_record"}},"created_at":"2026-05-17T23:46:08.671710+00:00","updated_at":"2026-05-17T23:46:08.671710+00:00"}