{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:PMOTOMP3ION5VENQLPC3WECBCT","short_pith_number":"pith:PMOTOMP3","canonical_record":{"source":{"id":"2501.10157","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-17T12:42:30Z","cross_cats_sorted":[],"title_canon_sha256":"a638e794265aa6d12eb24dadc54b5f27b5cb64ddae706eb923a664b8acf887d0","abstract_canon_sha256":"3e52308056eea651835e7d1ecee5d157dd93a7461da09d9d1d3804c8cd97efaf"},"schema_version":"1.0"},"canonical_sha256":"7b1d3731fb439bda91b05bc5bb104114f411a4885d8b0e24c6c1d738993003e2","source":{"kind":"arxiv","id":"2501.10157","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10157","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10157v3","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10157","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"pith_short_12","alias_value":"PMOTOMP3ION5","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"PMOTOMP3ION5VENQ","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"PMOTOMP3","created_at":"2026-07-05T10:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:PMOTOMP3ION5VENQLPC3WECBCT","target":"record","payload":{"canonical_record":{"source":{"id":"2501.10157","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-17T12:42:30Z","cross_cats_sorted":[],"title_canon_sha256":"a638e794265aa6d12eb24dadc54b5f27b5cb64ddae706eb923a664b8acf887d0","abstract_canon_sha256":"3e52308056eea651835e7d1ecee5d157dd93a7461da09d9d1d3804c8cd97efaf"},"schema_version":"1.0"},"canonical_sha256":"7b1d3731fb439bda91b05bc5bb104114f411a4885d8b0e24c6c1d738993003e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:31:00.674913Z","signature_b64":"CbxLKSSrn46y2G/PrJKsSF7HXdpmZWvF87JVgOoE17TiNdtvNWwz8qUsUoxdYuvtPaKsOU50XolgqE0mWkY7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b1d3731fb439bda91b05bc5bb104114f411a4885d8b0e24c6c1d738993003e2","last_reissued_at":"2026-07-05T10:31:00.673997Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:31:00.673997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.10157","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:31:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nHIAj/i/VhZZJ3qytR9QKdBOR4sDYTLsJkdjlRXX9hiEL6pZ6ZX6LeMqrk1kFLv4AVPp4sUFD5cpvwCkrmCWDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:18:33.501457Z"},"content_sha256":"3d72c941bfb4f0ff3522aa45356ecccfb173d1eb600b21cc240b9f2f784d9917","schema_version":"1.0","event_id":"sha256:3d72c941bfb4f0ff3522aa45356ecccfb173d1eb600b21cc240b9f2f784d9917"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:PMOTOMP3ION5VENQLPC3WECBCT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Structure-guided Deep Multi-View Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chongjie Dong, Haitao Zhang, Jie Wen, Jinrong Cui, Xiaohuang Wu","submitted_at":"2025-01-17T12:42:30Z","abstract_excerpt":"Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. However, most of the existing clustering methods often neglect to fully mine multi-view structural information and fail to explore the distribution of multi-view data, limiting clustering performance. To address these limitations, we propose a structure-guided deep multi-view clustering model. Specifically, we introduce a positive sample selection strategy based on neighborhood relationships, coupled with a corresponding loss function. This strategy constructs multi-view "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10157","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.10157/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:31:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hsyru52xkPmhS1tykvdxjGjPx1ZRpUlG3rXx0ZNcEXKfYFOSiMLS7YebmuWljDfE+gLh973Z3G87RkqDQlswCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T03:18:33.501946Z"},"content_sha256":"5e34985e986fe8a8d95fe689a5d3e9313a5c75f060d8d96b99d3b9c1babfe5ca","schema_version":"1.0","event_id":"sha256:5e34985e986fe8a8d95fe689a5d3e9313a5c75f060d8d96b99d3b9c1babfe5ca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PMOTOMP3ION5VENQLPC3WECBCT/bundle.json","state_url":"https://pith.science/pith/PMOTOMP3ION5VENQLPC3WECBCT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PMOTOMP3ION5VENQLPC3WECBCT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T03:18:33Z","links":{"resolver":"https://pith.science/pith/PMOTOMP3ION5VENQLPC3WECBCT","bundle":"https://pith.science/pith/PMOTOMP3ION5VENQLPC3WECBCT/bundle.json","state":"https://pith.science/pith/PMOTOMP3ION5VENQLPC3WECBCT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PMOTOMP3ION5VENQLPC3WECBCT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:PMOTOMP3ION5VENQLPC3WECBCT","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":"3e52308056eea651835e7d1ecee5d157dd93a7461da09d9d1d3804c8cd97efaf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-17T12:42:30Z","title_canon_sha256":"a638e794265aa6d12eb24dadc54b5f27b5cb64ddae706eb923a664b8acf887d0"},"schema_version":"1.0","source":{"id":"2501.10157","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.10157","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"arxiv_version","alias_value":"2501.10157v3","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.10157","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"pith_short_12","alias_value":"PMOTOMP3ION5","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"PMOTOMP3ION5VENQ","created_at":"2026-07-05T10:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"PMOTOMP3","created_at":"2026-07-05T10:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:5e34985e986fe8a8d95fe689a5d3e9313a5c75f060d8d96b99d3b9c1babfe5ca","target":"graph","created_at":"2026-07-05T10:31:00Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.10157/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. However, most of the existing clustering methods often neglect to fully mine multi-view structural information and fail to explore the distribution of multi-view data, limiting clustering performance. To address these limitations, we propose a structure-guided deep multi-view clustering model. Specifically, we introduce a positive sample selection strategy based on neighborhood relationships, coupled with a corresponding loss function. This strategy constructs multi-view ","authors_text":"Chongjie Dong, Haitao Zhang, Jie Wen, Jinrong Cui, Xiaohuang Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-17T12:42:30Z","title":"Structure-guided Deep Multi-View Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.10157","kind":"arxiv","version":3},"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":"sha256:3d72c941bfb4f0ff3522aa45356ecccfb173d1eb600b21cc240b9f2f784d9917","target":"record","created_at":"2026-07-05T10:31:00Z","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":"3e52308056eea651835e7d1ecee5d157dd93a7461da09d9d1d3804c8cd97efaf","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-17T12:42:30Z","title_canon_sha256":"a638e794265aa6d12eb24dadc54b5f27b5cb64ddae706eb923a664b8acf887d0"},"schema_version":"1.0","source":{"id":"2501.10157","kind":"arxiv","version":3}},"canonical_sha256":"7b1d3731fb439bda91b05bc5bb104114f411a4885d8b0e24c6c1d738993003e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7b1d3731fb439bda91b05bc5bb104114f411a4885d8b0e24c6c1d738993003e2","first_computed_at":"2026-07-05T10:31:00.673997Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:31:00.673997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CbxLKSSrn46y2G/PrJKsSF7HXdpmZWvF87JVgOoE17TiNdtvNWwz8qUsUoxdYuvtPaKsOU50XolgqE0mWkY7Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:31:00.674913Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.10157","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d72c941bfb4f0ff3522aa45356ecccfb173d1eb600b21cc240b9f2f784d9917","sha256:5e34985e986fe8a8d95fe689a5d3e9313a5c75f060d8d96b99d3b9c1babfe5ca"],"state_sha256":"209dbce398feb937271dcccfe9e3b0ef4b080f43cad6f79affff8340784c2b1c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a0te+uXHE0BRBRIzjTWWlaTZo9F4LyRs2nqzDMQSXxCn8c02rXPCV1DMBhmSOuPFDTxl2Ibdraut7xA5QZ7DCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T03:18:33.506344Z","bundle_sha256":"b26174e1c204f97c4df122378142bbef5b005f81a29b4356303142b922a65491"}}