{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:MTQD3YML2J6WONBN73G4UT74B5","short_pith_number":"pith:MTQD3YML","canonical_record":{"source":{"id":"2011.10331","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T10:37:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"96fddabf77edcf8c08ca1e8df027022ca0a5218f6c6cd6d0cf842b82b8af0059","abstract_canon_sha256":"cd70492920049b4159a074fe80a65dcab61b7c38728667c24a7e53a53ccb07e7"},"schema_version":"1.0"},"canonical_sha256":"64e03de18bd27d67342dfecdca4ffc0f67f70e593c395ae57685e0f25abeae1d","source":{"kind":"arxiv","id":"2011.10331","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.10331","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"arxiv_version","alias_value":"2011.10331v4","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.10331","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"pith_short_12","alias_value":"MTQD3YML2J6W","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"pith_short_16","alias_value":"MTQD3YML2J6WONBN","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"pith_short_8","alias_value":"MTQD3YML","created_at":"2026-05-26T02:03:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:MTQD3YML2J6WONBN73G4UT74B5","target":"record","payload":{"canonical_record":{"source":{"id":"2011.10331","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T10:37:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"96fddabf77edcf8c08ca1e8df027022ca0a5218f6c6cd6d0cf842b82b8af0059","abstract_canon_sha256":"cd70492920049b4159a074fe80a65dcab61b7c38728667c24a7e53a53ccb07e7"},"schema_version":"1.0"},"canonical_sha256":"64e03de18bd27d67342dfecdca4ffc0f67f70e593c395ae57685e0f25abeae1d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T02:03:43.908606Z","signature_b64":"Y4VSfpqtr9wHH/TyTCd1Z3ZueZ4CdUF1K2STg4Fec/9ulC3rKMhhPD6wiyhKBzRMyDYehkgc4oseeGerM+e0Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64e03de18bd27d67342dfecdca4ffc0f67f70e593c395ae57685e0f25abeae1d","last_reissued_at":"2026-05-26T02:03:43.907814Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T02:03:43.907814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.10331","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-26T02:03:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N3Bv8Pb/LrNHUIOhkIATuNPE4DZI8LCSqw4U0Q5d5qugp+tiVMfQ1Ezkl6MRmYrRS/eDO/iSfVBdthz2SnYeBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T18:40:01.840837Z"},"content_sha256":"fa801559777b0812a922af33e4f8cf74d9cb8d2001fd5547154edd9cade5e82d","schema_version":"1.0","event_id":"sha256:fa801559777b0812a922af33e4f8cf74d9cb8d2001fd5547154edd9cade5e82d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:MTQD3YML2J6WONBN73G4UT74B5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Dapeng Oliver Wu, Pan Zhou, Xiang Fang, Yuchong Hu","submitted_at":"2020-11-20T10:37:27Z","abstract_excerpt":"Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative ma"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.10331","kind":"arxiv","version":4},"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/2011.10331/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":"2026-05-26T02:03:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R0qzKWRqoGJmTCaGu1Fb9JTjYe7B8ZVNsIdGGdio5rPKamA0eDhLIb76f4EvdF2KQWqpJ6DndhCmOxLzM1EIAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T18:40:01.841221Z"},"content_sha256":"0d8627a673b5ab01f46b3fd20aa0389113462ee1dc38e4644caaecc6ebee7019","schema_version":"1.0","event_id":"sha256:0d8627a673b5ab01f46b3fd20aa0389113462ee1dc38e4644caaecc6ebee7019"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MTQD3YML2J6WONBN73G4UT74B5/bundle.json","state_url":"https://pith.science/pith/MTQD3YML2J6WONBN73G4UT74B5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MTQD3YML2J6WONBN73G4UT74B5/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":"2026-06-03T18:40:01Z","links":{"resolver":"https://pith.science/pith/MTQD3YML2J6WONBN73G4UT74B5","bundle":"https://pith.science/pith/MTQD3YML2J6WONBN73G4UT74B5/bundle.json","state":"https://pith.science/pith/MTQD3YML2J6WONBN73G4UT74B5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MTQD3YML2J6WONBN73G4UT74B5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:MTQD3YML2J6WONBN73G4UT74B5","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":"cd70492920049b4159a074fe80a65dcab61b7c38728667c24a7e53a53ccb07e7","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T10:37:27Z","title_canon_sha256":"96fddabf77edcf8c08ca1e8df027022ca0a5218f6c6cd6d0cf842b82b8af0059"},"schema_version":"1.0","source":{"id":"2011.10331","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.10331","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"arxiv_version","alias_value":"2011.10331v4","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.10331","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"pith_short_12","alias_value":"MTQD3YML2J6W","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"pith_short_16","alias_value":"MTQD3YML2J6WONBN","created_at":"2026-05-26T02:03:43Z"},{"alias_kind":"pith_short_8","alias_value":"MTQD3YML","created_at":"2026-05-26T02:03:43Z"}],"graph_snapshots":[{"event_id":"sha256:0d8627a673b5ab01f46b3fd20aa0389113462ee1dc38e4644caaecc6ebee7019","target":"graph","created_at":"2026-05-26T02:03:43Z","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/2011.10331/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing instances and noises, which is ignored by most multi-view clustering methods. However, missing instances may make these methods difficult to use directly and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative ma","authors_text":"Dapeng Oliver Wu, Pan Zhou, Xiang Fang, Yuchong Hu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T10:37:27Z","title":"ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.10331","kind":"arxiv","version":4},"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:fa801559777b0812a922af33e4f8cf74d9cb8d2001fd5547154edd9cade5e82d","target":"record","created_at":"2026-05-26T02:03:43Z","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":"cd70492920049b4159a074fe80a65dcab61b7c38728667c24a7e53a53ccb07e7","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-11-20T10:37:27Z","title_canon_sha256":"96fddabf77edcf8c08ca1e8df027022ca0a5218f6c6cd6d0cf842b82b8af0059"},"schema_version":"1.0","source":{"id":"2011.10331","kind":"arxiv","version":4}},"canonical_sha256":"64e03de18bd27d67342dfecdca4ffc0f67f70e593c395ae57685e0f25abeae1d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64e03de18bd27d67342dfecdca4ffc0f67f70e593c395ae57685e0f25abeae1d","first_computed_at":"2026-05-26T02:03:43.907814Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-26T02:03:43.907814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y4VSfpqtr9wHH/TyTCd1Z3ZueZ4CdUF1K2STg4Fec/9ulC3rKMhhPD6wiyhKBzRMyDYehkgc4oseeGerM+e0Dg==","signature_status":"signed_v1","signed_at":"2026-05-26T02:03:43.908606Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.10331","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa801559777b0812a922af33e4f8cf74d9cb8d2001fd5547154edd9cade5e82d","sha256:0d8627a673b5ab01f46b3fd20aa0389113462ee1dc38e4644caaecc6ebee7019"],"state_sha256":"f8eb87b1a79d2cb86bf5411f6f0fff2009cbc8b8fd17ce408a56d5aac74a4c48"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LiNYdVBYJF7MesMCwGvWTGZkQxamH7RNwpy8D2+FiL4nCPkOmTFcnlKMFMV6vpMb+Fcobf2hkD0udg0M7ND7Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T18:40:01.843428Z","bundle_sha256":"9096a4899bd8717c2e272c8dd407d8c4c0e0e6860eeaa20d10a3321a44b4359a"}}