{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:RCXSL6ROFGSSXLOXO5JPH6JCGT","short_pith_number":"pith:RCXSL6RO","canonical_record":{"source":{"id":"2004.01143","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-04-02T17:15:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"588437f83007d74ac336702ca45bee0423486892e4ac6b983ca26ca151a7f8d9","abstract_canon_sha256":"a31525c7d2aa333d5b2486ee141949623f30d4a59b422df0db276c40cb12a2e1"},"schema_version":"1.0"},"canonical_sha256":"88af25fa2e29a52badd77752f3f92234c6a5ab184c9c1aa3c632d3e6451a2ff6","source":{"kind":"arxiv","id":"2004.01143","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.01143","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"arxiv_version","alias_value":"2004.01143v1","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.01143","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"pith_short_12","alias_value":"RCXSL6ROFGSS","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"pith_short_16","alias_value":"RCXSL6ROFGSSXLOX","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"pith_short_8","alias_value":"RCXSL6RO","created_at":"2026-07-05T00:52:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:RCXSL6ROFGSSXLOXO5JPH6JCGT","target":"record","payload":{"canonical_record":{"source":{"id":"2004.01143","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-04-02T17:15:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"588437f83007d74ac336702ca45bee0423486892e4ac6b983ca26ca151a7f8d9","abstract_canon_sha256":"a31525c7d2aa333d5b2486ee141949623f30d4a59b422df0db276c40cb12a2e1"},"schema_version":"1.0"},"canonical_sha256":"88af25fa2e29a52badd77752f3f92234c6a5ab184c9c1aa3c632d3e6451a2ff6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:52:24.603116Z","signature_b64":"eiiEByD3plbsPjwnKCgvDBKuHMVX0dMcUnpoS+hf69OrmMfefJnplzVdiyofjbm6e5i1RIWQTBmeZvTEivzrCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88af25fa2e29a52badd77752f3f92234c6a5ab184c9c1aa3c632d3e6451a2ff6","last_reissued_at":"2026-07-05T00:52:24.602757Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:52:24.602757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2004.01143","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":"2026-07-05T00:52:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VX4luZgXiKOg/6TNdYABsCn9JshCNCMq+ppOBsE5opJayGhvYWZZ4wvbFsU5a9zW9x+Iq0e/D6x+bcKX3NTMDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T14:59:47.417235Z"},"content_sha256":"87a0320adb687d9ddbc42bd5f68aa6401686e12761b5c17b45728f1f311efcb7","schema_version":"1.0","event_id":"sha256:87a0320adb687d9ddbc42bd5f68aa6401686e12761b5c17b45728f1f311efcb7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:RCXSL6ROFGSSXLOXO5JPH6JCGT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Randomized Kernel Multi-view Discriminant Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jie Gui, Ping Li, Xiaoyun Li","submitted_at":"2020-04-02T17:15:32Z","abstract_excerpt":"In many artificial intelligence and computer vision systems, the same object can be observed at distinct viewpoints or by diverse sensors, which raises the challenges for recognizing objects from different, even heterogeneous views. Multi-view discriminant analysis (MvDA) is an effective multi-view subspace learning method, which finds a discriminant common subspace by jointly learning multiple view-specific linear projections for object recognition from multiple views, in a non-pairwise way. In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.01143","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/2004.01143/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":"2026-07-05T00:52:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZH5Q6QyOofYnQSsYzdP6T2qr9xWBji2Mf5nAqZpNFD65FuerkZ75WpjvIaWTNECAu91jaEecUpacXHmWmiptBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T14:59:47.418031Z"},"content_sha256":"5a8cf52084536bdcc5b7738a3a9d396c41599e3187d1fadce716af1549051116","schema_version":"1.0","event_id":"sha256:5a8cf52084536bdcc5b7738a3a9d396c41599e3187d1fadce716af1549051116"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RCXSL6ROFGSSXLOXO5JPH6JCGT/bundle.json","state_url":"https://pith.science/pith/RCXSL6ROFGSSXLOXO5JPH6JCGT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RCXSL6ROFGSSXLOXO5JPH6JCGT/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":"2026-07-05T14:59:47Z","links":{"resolver":"https://pith.science/pith/RCXSL6ROFGSSXLOXO5JPH6JCGT","bundle":"https://pith.science/pith/RCXSL6ROFGSSXLOXO5JPH6JCGT/bundle.json","state":"https://pith.science/pith/RCXSL6ROFGSSXLOXO5JPH6JCGT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RCXSL6ROFGSSXLOXO5JPH6JCGT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:RCXSL6ROFGSSXLOXO5JPH6JCGT","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":"a31525c7d2aa333d5b2486ee141949623f30d4a59b422df0db276c40cb12a2e1","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-04-02T17:15:32Z","title_canon_sha256":"588437f83007d74ac336702ca45bee0423486892e4ac6b983ca26ca151a7f8d9"},"schema_version":"1.0","source":{"id":"2004.01143","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.01143","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"arxiv_version","alias_value":"2004.01143v1","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.01143","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"pith_short_12","alias_value":"RCXSL6ROFGSS","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"pith_short_16","alias_value":"RCXSL6ROFGSSXLOX","created_at":"2026-07-05T00:52:24Z"},{"alias_kind":"pith_short_8","alias_value":"RCXSL6RO","created_at":"2026-07-05T00:52:24Z"}],"graph_snapshots":[{"event_id":"sha256:5a8cf52084536bdcc5b7738a3a9d396c41599e3187d1fadce716af1549051116","target":"graph","created_at":"2026-07-05T00:52:24Z","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/2004.01143/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In many artificial intelligence and computer vision systems, the same object can be observed at distinct viewpoints or by diverse sensors, which raises the challenges for recognizing objects from different, even heterogeneous views. Multi-view discriminant analysis (MvDA) is an effective multi-view subspace learning method, which finds a discriminant common subspace by jointly learning multiple view-specific linear projections for object recognition from multiple views, in a non-pairwise way. In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-","authors_text":"Jie Gui, Ping Li, Xiaoyun Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-04-02T17:15:32Z","title":"Randomized Kernel Multi-view Discriminant Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.01143","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":"sha256:87a0320adb687d9ddbc42bd5f68aa6401686e12761b5c17b45728f1f311efcb7","target":"record","created_at":"2026-07-05T00:52:24Z","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":"a31525c7d2aa333d5b2486ee141949623f30d4a59b422df0db276c40cb12a2e1","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-04-02T17:15:32Z","title_canon_sha256":"588437f83007d74ac336702ca45bee0423486892e4ac6b983ca26ca151a7f8d9"},"schema_version":"1.0","source":{"id":"2004.01143","kind":"arxiv","version":1}},"canonical_sha256":"88af25fa2e29a52badd77752f3f92234c6a5ab184c9c1aa3c632d3e6451a2ff6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"88af25fa2e29a52badd77752f3f92234c6a5ab184c9c1aa3c632d3e6451a2ff6","first_computed_at":"2026-07-05T00:52:24.602757Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:52:24.602757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eiiEByD3plbsPjwnKCgvDBKuHMVX0dMcUnpoS+hf69OrmMfefJnplzVdiyofjbm6e5i1RIWQTBmeZvTEivzrCA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:52:24.603116Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.01143","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:87a0320adb687d9ddbc42bd5f68aa6401686e12761b5c17b45728f1f311efcb7","sha256:5a8cf52084536bdcc5b7738a3a9d396c41599e3187d1fadce716af1549051116"],"state_sha256":"8b2761d4d35884f9ae17c71c40ee4c616be6d285561be8cc0fc1ce4d51b5e7f5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5wVpN+Wv36qwKov9H0gLBcAtMzhUPfbKpUmhE/VUuuV6uxgJgiJMF2joNVeCYJQWqvP62CAk2vliW4L6rN+jAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T14:59:47.423190Z","bundle_sha256":"1721b7ed74dd4db0d2fb07455c3778ce79a25a5473d9e02bcdbb5e2daa99d01f"}}