{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:LWQU3PZN57O4T277HWCODBMGQ4","short_pith_number":"pith:LWQU3PZN","canonical_record":{"source":{"id":"1711.05068","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-14T12:00:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0095f25f0057dfe0f4562fbc62e84bf4c7b02dd1dc253757b3753839d44aada8","abstract_canon_sha256":"a53b8672849a56e4f3e084d5db46d2c9b84f1b22f299947f94cc009c95f36d63"},"schema_version":"1.0"},"canonical_sha256":"5da14dbf2defddc9ebff3d84e18586873476c809d5a67cf1553a87dfd6ba1143","source":{"kind":"arxiv","id":"1711.05068","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05068","created_at":"2026-05-18T00:30:29Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05068v2","created_at":"2026-05-18T00:30:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05068","created_at":"2026-05-18T00:30:29Z"},{"alias_kind":"pith_short_12","alias_value":"LWQU3PZN57O4","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LWQU3PZN57O4T277","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LWQU3PZN","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:LWQU3PZN57O4T277HWCODBMGQ4","target":"record","payload":{"canonical_record":{"source":{"id":"1711.05068","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-14T12:00:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0095f25f0057dfe0f4562fbc62e84bf4c7b02dd1dc253757b3753839d44aada8","abstract_canon_sha256":"a53b8672849a56e4f3e084d5db46d2c9b84f1b22f299947f94cc009c95f36d63"},"schema_version":"1.0"},"canonical_sha256":"5da14dbf2defddc9ebff3d84e18586873476c809d5a67cf1553a87dfd6ba1143","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:29.876274Z","signature_b64":"hR1f+VWXrXq45p7ZZjk38cAnMzOABp3Fh1OoV2U7p1E1Ltq1bV1OLqtSodrE1jSnf8zcprfffulzo6sVS5CuAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5da14dbf2defddc9ebff3d84e18586873476c809d5a67cf1553a87dfd6ba1143","last_reissued_at":"2026-05-18T00:30:29.875587Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:29.875587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.05068","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:30:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"akFR8fXaZ3gS/EK6MEgrGpQ/aiopCwSJnIp6i3JaXjMDN0IBjInFOrPMRszUgIPbgZwu/jV/h8yjiwD+2vlWAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T02:05:18.911357Z"},"content_sha256":"13ec9f32bb02f14623ceb41c909d4274cafb2f3ffe3774d1c471a06267e8a16a","schema_version":"1.0","event_id":"sha256:13ec9f32bb02f14623ceb41c909d4274cafb2f3ffe3774d1c471a06267e8a16a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:LWQU3PZN57O4T277HWCODBMGQ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Peng-Bo Zhang, Zhi-Xin Yang","submitted_at":"2017-11-14T12:00:53Z","abstract_excerpt":"This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different 'views'. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the k"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05068","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:30:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LZXHiQLRmOdlfbFztBU0PT6DNiOw4ghdvLubCV3G/7MCPWjtmEJILI1FbMSN7hNy7IvVoLNrJOZDZ8jTRPRxBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T02:05:18.911937Z"},"content_sha256":"2f5713ef69711076c760fb175fe56957e301729106cf198bf46019730b25955a","schema_version":"1.0","event_id":"sha256:2f5713ef69711076c760fb175fe56957e301729106cf198bf46019730b25955a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LWQU3PZN57O4T277HWCODBMGQ4/bundle.json","state_url":"https://pith.science/pith/LWQU3PZN57O4T277HWCODBMGQ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LWQU3PZN57O4T277HWCODBMGQ4/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":"2026-06-11T02:05:18Z","links":{"resolver":"https://pith.science/pith/LWQU3PZN57O4T277HWCODBMGQ4","bundle":"https://pith.science/pith/LWQU3PZN57O4T277HWCODBMGQ4/bundle.json","state":"https://pith.science/pith/LWQU3PZN57O4T277HWCODBMGQ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LWQU3PZN57O4T277HWCODBMGQ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:LWQU3PZN57O4T277HWCODBMGQ4","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":"a53b8672849a56e4f3e084d5db46d2c9b84f1b22f299947f94cc009c95f36d63","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-14T12:00:53Z","title_canon_sha256":"0095f25f0057dfe0f4562fbc62e84bf4c7b02dd1dc253757b3753839d44aada8"},"schema_version":"1.0","source":{"id":"1711.05068","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.05068","created_at":"2026-05-18T00:30:29Z"},{"alias_kind":"arxiv_version","alias_value":"1711.05068v2","created_at":"2026-05-18T00:30:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.05068","created_at":"2026-05-18T00:30:29Z"},{"alias_kind":"pith_short_12","alias_value":"LWQU3PZN57O4","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LWQU3PZN57O4T277","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LWQU3PZN","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:2f5713ef69711076c760fb175fe56957e301729106cf198bf46019730b25955a","target":"graph","created_at":"2026-05-18T00:30:29Z","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"},"paper":{"abstract_excerpt":"This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different 'views'. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the k","authors_text":"Peng-Bo Zhang, Zhi-Xin Yang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-14T12:00:53Z","title":"Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.05068","kind":"arxiv","version":2},"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":"sha256:13ec9f32bb02f14623ceb41c909d4274cafb2f3ffe3774d1c471a06267e8a16a","target":"record","created_at":"2026-05-18T00:30:29Z","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":"a53b8672849a56e4f3e084d5db46d2c9b84f1b22f299947f94cc009c95f36d63","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-14T12:00:53Z","title_canon_sha256":"0095f25f0057dfe0f4562fbc62e84bf4c7b02dd1dc253757b3753839d44aada8"},"schema_version":"1.0","source":{"id":"1711.05068","kind":"arxiv","version":2}},"canonical_sha256":"5da14dbf2defddc9ebff3d84e18586873476c809d5a67cf1553a87dfd6ba1143","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5da14dbf2defddc9ebff3d84e18586873476c809d5a67cf1553a87dfd6ba1143","first_computed_at":"2026-05-18T00:30:29.875587Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:30:29.875587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hR1f+VWXrXq45p7ZZjk38cAnMzOABp3Fh1OoV2U7p1E1Ltq1bV1OLqtSodrE1jSnf8zcprfffulzo6sVS5CuAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:30:29.876274Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.05068","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:13ec9f32bb02f14623ceb41c909d4274cafb2f3ffe3774d1c471a06267e8a16a","sha256:2f5713ef69711076c760fb175fe56957e301729106cf198bf46019730b25955a"],"state_sha256":"d8b62357054e17d8c297ced0c722ddb534a42d2252d1c79e353b6aaa286d601c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lohIKkWs0iRlTSzZjOIOdqZP7OmLi1EOE4hedrhHK0lEUTotp+RGjqsUw5ZBIIFEi5E4GhxKwbJjlZdo36nSDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T02:05:18.915518Z","bundle_sha256":"c26ca5c906d49d5158320fab814b24b8e8bb16f49e5b0cfa4b71535c95a9a18f"}}