{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XQA22FZ3BUH7QV2SIN3QQTTCLD","short_pith_number":"pith:XQA22FZ3","schema_version":"1.0","canonical_sha256":"bc01ad173b0d0ff857524377084e6258c4e8f4db0b7073e74655413b7889087b","source":{"kind":"arxiv","id":"1907.09747","version":1},"attestation_state":"computed","paper":{"title":"Shared Generative Latent Representation Learning for Multi-view Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junbin Gao, Ming Yin, Weitian Huang","submitted_at":"2019-07-23T08:20:38Z","abstract_excerpt":"Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the mul"},"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":"1907.09747","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-23T08:20:38Z","cross_cats_sorted":[],"title_canon_sha256":"000d4a344d6489efee00b2875af2e55eaf23564cd5123220d28bd35422f14d01","abstract_canon_sha256":"032c8ccb5e6a6d45f44786066b6d847a81d8e993c66e586fb82c3ee648aacd00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:50.185512Z","signature_b64":"NcY61LJL42QOiNxAirAa066Nd/tZL1hL7d/L5OeTZ/IqZ/GcDb3p4kg/s279eA3Val1eanH3+RPDh0865ENBDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc01ad173b0d0ff857524377084e6258c4e8f4db0b7073e74655413b7889087b","last_reissued_at":"2026-05-17T23:39:50.184867Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:50.184867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Shared Generative Latent Representation Learning for Multi-view Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junbin Gao, Ming Yin, Weitian Huang","submitted_at":"2019-07-23T08:20:38Z","abstract_excerpt":"Clustering multi-view data has been a fundamental research topic in the computer vision community. It has been shown that a better accuracy can be achieved by integrating information of all the views than just using one view individually. However, the existing methods often struggle with the issues of dealing with the large-scale datasets and the poor performance in reconstructing samples. This paper proposes a novel multi-view clustering method by learning a shared generative latent representation that obeys a mixture of Gaussian distributions. The motivation is based on the fact that the mul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.09747","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":"1907.09747","created_at":"2026-05-17T23:39:50.184950+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.09747v1","created_at":"2026-05-17T23:39:50.184950+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.09747","created_at":"2026-05-17T23:39:50.184950+00:00"},{"alias_kind":"pith_short_12","alias_value":"XQA22FZ3BUH7","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"XQA22FZ3BUH7QV2S","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"XQA22FZ3","created_at":"2026-05-18T12:33:33.725879+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/XQA22FZ3BUH7QV2SIN3QQTTCLD","json":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD.json","graph_json":"https://pith.science/api/pith-number/XQA22FZ3BUH7QV2SIN3QQTTCLD/graph.json","events_json":"https://pith.science/api/pith-number/XQA22FZ3BUH7QV2SIN3QQTTCLD/events.json","paper":"https://pith.science/paper/XQA22FZ3"},"agent_actions":{"view_html":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD","download_json":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD.json","view_paper":"https://pith.science/paper/XQA22FZ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.09747&json=true","fetch_graph":"https://pith.science/api/pith-number/XQA22FZ3BUH7QV2SIN3QQTTCLD/graph.json","fetch_events":"https://pith.science/api/pith-number/XQA22FZ3BUH7QV2SIN3QQTTCLD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD/action/storage_attestation","attest_author":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD/action/author_attestation","sign_citation":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD/action/citation_signature","submit_replication":"https://pith.science/pith/XQA22FZ3BUH7QV2SIN3QQTTCLD/action/replication_record"}},"created_at":"2026-05-17T23:39:50.184950+00:00","updated_at":"2026-05-17T23:39:50.184950+00:00"}