{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4GS3IGH2ZQF3LNBIACDOBQE42Z","short_pith_number":"pith:4GS3IGH2","canonical_record":{"source":{"id":"2505.21387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T16:16:54Z","cross_cats_sorted":[],"title_canon_sha256":"8d849a2fe70ef10f988bc3f8a2b721791287d194ccd807ecb51fd87b1f66476c","abstract_canon_sha256":"d406f52413733a082339e9d856d2514c9eff495dc85c87aa332ae1c246727d22"},"schema_version":"1.0"},"canonical_sha256":"e1a5b418facc0bb5b4280086e0c09cd651b6fbf9a949e905bf8a75c3b300ba5c","source":{"kind":"arxiv","id":"2505.21387","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.21387","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.21387v1","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21387","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"pith_short_12","alias_value":"4GS3IGH2ZQF3","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"pith_short_16","alias_value":"4GS3IGH2ZQF3LNBI","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"pith_short_8","alias_value":"4GS3IGH2","created_at":"2026-07-05T11:10:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4GS3IGH2ZQF3LNBIACDOBQE42Z","target":"record","payload":{"canonical_record":{"source":{"id":"2505.21387","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T16:16:54Z","cross_cats_sorted":[],"title_canon_sha256":"8d849a2fe70ef10f988bc3f8a2b721791287d194ccd807ecb51fd87b1f66476c","abstract_canon_sha256":"d406f52413733a082339e9d856d2514c9eff495dc85c87aa332ae1c246727d22"},"schema_version":"1.0"},"canonical_sha256":"e1a5b418facc0bb5b4280086e0c09cd651b6fbf9a949e905bf8a75c3b300ba5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:41.191703Z","signature_b64":"emZpcUwCQ2av2TvmhdeQZJcFP9kURyr4gQ7iVORMOze/1zBpgwdu4b7ZAPqGpcIY4IzHbWztD8gY+ERCiNtLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1a5b418facc0bb5b4280086e0c09cd651b6fbf9a949e905bf8a75c3b300ba5c","last_reissued_at":"2026-07-05T11:10:41.191132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:41.191132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.21387","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:10:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v8Uaj43USNeFk+T5bp+czOetQ7GwZ26hf0FYZQf6lKrazpB7A6yz0CKvRslKhqMkWSbxfO+Kn9WUCkyGVuzQAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:54:11.945494Z"},"content_sha256":"76ad020ea52890d40a62227bb82eefbc5d8547343a04330f89d147edd2a878c6","schema_version":"1.0","event_id":"sha256:76ad020ea52890d40a62227bb82eefbc5d8547343a04330f89d147edd2a878c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4GS3IGH2ZQF3LNBIACDOBQE42Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"En Zhu, Fangdi Wang, Jiaqi Jin, Siwei Wang, Suyuan Liu, Xihong Yang, Xinwang Liu, Yue Liu, Yueming Jin","submitted_at":"2025-05-27T16:16:54Z","abstract_excerpt":"Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real-world scenarios, leading to a significant degradation in performance. To tackle this problem, we propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC. Specifically, we reformulate noisy identification as "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21387","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.21387/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":"2026-07-05T11:10:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p/AuNgh3nfqN0mchGxWDQDNidbTjuXI6tumQdJFJR+3Eq68y/G5C33fbXEoO52+4+TD72rxCjyHCtMjgDtz7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:54:11.946253Z"},"content_sha256":"df3a3eae3214dc4275a25167c2328635c91c03c55436a146847c77bcffab8b1a","schema_version":"1.0","event_id":"sha256:df3a3eae3214dc4275a25167c2328635c91c03c55436a146847c77bcffab8b1a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/bundle.json","state_url":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/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":"2026-07-09T06:54:11Z","links":{"resolver":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z","bundle":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/bundle.json","state":"https://pith.science/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4GS3IGH2ZQF3LNBIACDOBQE42Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4GS3IGH2ZQF3LNBIACDOBQE42Z","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":"d406f52413733a082339e9d856d2514c9eff495dc85c87aa332ae1c246727d22","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T16:16:54Z","title_canon_sha256":"8d849a2fe70ef10f988bc3f8a2b721791287d194ccd807ecb51fd87b1f66476c"},"schema_version":"1.0","source":{"id":"2505.21387","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.21387","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.21387v1","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21387","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"pith_short_12","alias_value":"4GS3IGH2ZQF3","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"pith_short_16","alias_value":"4GS3IGH2ZQF3LNBI","created_at":"2026-07-05T11:10:41Z"},{"alias_kind":"pith_short_8","alias_value":"4GS3IGH2","created_at":"2026-07-05T11:10:41Z"}],"graph_snapshots":[{"event_id":"sha256:df3a3eae3214dc4275a25167c2328635c91c03c55436a146847c77bcffab8b1a","target":"graph","created_at":"2026-07-05T11:10:41Z","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/2505.21387/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source information from diverse views in recent years. Most existing methods rely on the assumption of clean views. However, noise is pervasive in real-world scenarios, leading to a significant degradation in performance. To tackle this problem, we propose a novel multi-view clustering framework for the automatic identification and rectification of noisy data, termed AIRMVC. Specifically, we reformulate noisy identification as ","authors_text":"En Zhu, Fangdi Wang, Jiaqi Jin, Siwei Wang, Suyuan Liu, Xihong Yang, Xinwang Liu, Yue Liu, Yueming Jin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T16:16:54Z","title":"Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21387","kind":"arxiv","version":1},"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":"sha256:76ad020ea52890d40a62227bb82eefbc5d8547343a04330f89d147edd2a878c6","target":"record","created_at":"2026-07-05T11:10:41Z","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":"d406f52413733a082339e9d856d2514c9eff495dc85c87aa332ae1c246727d22","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T16:16:54Z","title_canon_sha256":"8d849a2fe70ef10f988bc3f8a2b721791287d194ccd807ecb51fd87b1f66476c"},"schema_version":"1.0","source":{"id":"2505.21387","kind":"arxiv","version":1}},"canonical_sha256":"e1a5b418facc0bb5b4280086e0c09cd651b6fbf9a949e905bf8a75c3b300ba5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e1a5b418facc0bb5b4280086e0c09cd651b6fbf9a949e905bf8a75c3b300ba5c","first_computed_at":"2026-07-05T11:10:41.191132Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:10:41.191132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"emZpcUwCQ2av2TvmhdeQZJcFP9kURyr4gQ7iVORMOze/1zBpgwdu4b7ZAPqGpcIY4IzHbWztD8gY+ERCiNtLDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:10:41.191703Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.21387","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76ad020ea52890d40a62227bb82eefbc5d8547343a04330f89d147edd2a878c6","sha256:df3a3eae3214dc4275a25167c2328635c91c03c55436a146847c77bcffab8b1a"],"state_sha256":"223ff08cea5d73bfcdbfc7797926dddba31f066548735c5adb5a799d09858ef4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l7TOHyTnCya/f5gTRTMKwkj7mZdH2sXPtaDQZQ3Hq7P+J1DivIobQtGxg/Z3rYhTXJgmBGD6GS63E6naKWr+Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:54:11.949860Z","bundle_sha256":"c7f0e00b72046ac7d09995a41ad232d2fa8058403b5ae0e060994495e648d4b6"}}