{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:IC3XIXRTM6XO7ZQ7SKDSLEDNP6","short_pith_number":"pith:IC3XIXRT","canonical_record":{"source":{"id":"1511.04839","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-16T06:25:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e96eaad8a4eb5b70623f704d7ba60e7ce9a48cca0ac75c123f1bd915de29c92f","abstract_canon_sha256":"2f2f92a8de9a64f933cc3397641851f36ae8af48f182c4fd8e9114b3ba4d2313"},"schema_version":"1.0"},"canonical_sha256":"40b7745e3367aeefe61f928725906d7f8cdebf08f88f046e28af58dd128bc345","source":{"kind":"arxiv","id":"1511.04839","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.04839","created_at":"2026-05-18T01:21:11Z"},{"alias_kind":"arxiv_version","alias_value":"1511.04839v4","created_at":"2026-05-18T01:21:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.04839","created_at":"2026-05-18T01:21:11Z"},{"alias_kind":"pith_short_12","alias_value":"IC3XIXRTM6XO","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"IC3XIXRTM6XO7ZQ7","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"IC3XIXRT","created_at":"2026-05-18T12:29:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:IC3XIXRTM6XO7ZQ7SKDSLEDNP6","target":"record","payload":{"canonical_record":{"source":{"id":"1511.04839","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-16T06:25:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e96eaad8a4eb5b70623f704d7ba60e7ce9a48cca0ac75c123f1bd915de29c92f","abstract_canon_sha256":"2f2f92a8de9a64f933cc3397641851f36ae8af48f182c4fd8e9114b3ba4d2313"},"schema_version":"1.0"},"canonical_sha256":"40b7745e3367aeefe61f928725906d7f8cdebf08f88f046e28af58dd128bc345","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:21:11.166557Z","signature_b64":"PU5ZV6H77q/pZvWG3XP6JiLuYc/zJksalsLPxmKWtbPxsjMHaA6dUikgHCWNNe+3weXcIdk9lqnF5avPElOfBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40b7745e3367aeefe61f928725906d7f8cdebf08f88f046e28af58dd128bc345","last_reissued_at":"2026-05-18T01:21:11.165747Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:21:11.165747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1511.04839","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":"2026-05-18T01:21:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n9qIjxIrwMZEIlGd6RfetRMPM5F7C22BmOuJqZb1njnOoUff6twAScuqHOklCH9RMVxlr1iMEax8QAKffjMdBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T06:04:27.928237Z"},"content_sha256":"93e21364b16bec6efa5b0c61624ec37a17bf20e7845900b806c7e85b456e355c","schema_version":"1.0","event_id":"sha256:93e21364b16bec6efa5b0c61624ec37a17bf20e7845900b806c7e85b456e355c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:IC3XIXRTM6XO7ZQ7SKDSLEDNP6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Nonparametric Canonical Correlation Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Karen Livescu, Tomer Michaeli, Weiran Wang","submitted_at":"2015-11-16T06:25:59Z","abstract_excerpt":"Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel or neural network structure), and are computationally demanding. Interestingly, the theory of nonlinear CCA, without functional restrictions, had been studied in the population setting by Lancaster alread"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.04839","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":""},"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":"2026-05-18T01:21:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"esNjs+8g1IH7qJd7U17BE4stjpHoJHaQIoe97z6jRhrvkH/bBrNsapuJPs92LIjb0QrjcCKqP22hJ4M827AwDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T06:04:27.928912Z"},"content_sha256":"fc62025105d6337fee5c00c8559540e3de4382de5818748173bb4a50a0d9867b","schema_version":"1.0","event_id":"sha256:fc62025105d6337fee5c00c8559540e3de4382de5818748173bb4a50a0d9867b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IC3XIXRTM6XO7ZQ7SKDSLEDNP6/bundle.json","state_url":"https://pith.science/pith/IC3XIXRTM6XO7ZQ7SKDSLEDNP6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IC3XIXRTM6XO7ZQ7SKDSLEDNP6/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":"2026-05-23T06:04:27Z","links":{"resolver":"https://pith.science/pith/IC3XIXRTM6XO7ZQ7SKDSLEDNP6","bundle":"https://pith.science/pith/IC3XIXRTM6XO7ZQ7SKDSLEDNP6/bundle.json","state":"https://pith.science/pith/IC3XIXRTM6XO7ZQ7SKDSLEDNP6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IC3XIXRTM6XO7ZQ7SKDSLEDNP6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:IC3XIXRTM6XO7ZQ7SKDSLEDNP6","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":"2f2f92a8de9a64f933cc3397641851f36ae8af48f182c4fd8e9114b3ba4d2313","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-16T06:25:59Z","title_canon_sha256":"e96eaad8a4eb5b70623f704d7ba60e7ce9a48cca0ac75c123f1bd915de29c92f"},"schema_version":"1.0","source":{"id":"1511.04839","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1511.04839","created_at":"2026-05-18T01:21:11Z"},{"alias_kind":"arxiv_version","alias_value":"1511.04839v4","created_at":"2026-05-18T01:21:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.04839","created_at":"2026-05-18T01:21:11Z"},{"alias_kind":"pith_short_12","alias_value":"IC3XIXRTM6XO","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_16","alias_value":"IC3XIXRTM6XO7ZQ7","created_at":"2026-05-18T12:29:25Z"},{"alias_kind":"pith_short_8","alias_value":"IC3XIXRT","created_at":"2026-05-18T12:29:25Z"}],"graph_snapshots":[{"event_id":"sha256:fc62025105d6337fee5c00c8559540e3de4382de5818748173bb4a50a0d9867b","target":"graph","created_at":"2026-05-18T01:21:11Z","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":"Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections among families of functions, which the user specifies (by choosing a kernel or neural network structure), and are computationally demanding. Interestingly, the theory of nonlinear CCA, without functional restrictions, had been studied in the population setting by Lancaster alread","authors_text":"Karen Livescu, Tomer Michaeli, Weiran Wang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-16T06:25:59Z","title":"Nonparametric Canonical Correlation Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.04839","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":"sha256:93e21364b16bec6efa5b0c61624ec37a17bf20e7845900b806c7e85b456e355c","target":"record","created_at":"2026-05-18T01:21:11Z","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":"2f2f92a8de9a64f933cc3397641851f36ae8af48f182c4fd8e9114b3ba4d2313","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-16T06:25:59Z","title_canon_sha256":"e96eaad8a4eb5b70623f704d7ba60e7ce9a48cca0ac75c123f1bd915de29c92f"},"schema_version":"1.0","source":{"id":"1511.04839","kind":"arxiv","version":4}},"canonical_sha256":"40b7745e3367aeefe61f928725906d7f8cdebf08f88f046e28af58dd128bc345","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40b7745e3367aeefe61f928725906d7f8cdebf08f88f046e28af58dd128bc345","first_computed_at":"2026-05-18T01:21:11.165747Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:21:11.165747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PU5ZV6H77q/pZvWG3XP6JiLuYc/zJksalsLPxmKWtbPxsjMHaA6dUikgHCWNNe+3weXcIdk9lqnF5avPElOfBw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:21:11.166557Z","signed_message":"canonical_sha256_bytes"},"source_id":"1511.04839","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93e21364b16bec6efa5b0c61624ec37a17bf20e7845900b806c7e85b456e355c","sha256:fc62025105d6337fee5c00c8559540e3de4382de5818748173bb4a50a0d9867b"],"state_sha256":"b293b28f8dec1cb19d589a5454a836673303a54c9586b7de708b9101bea15e0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"etUJpCtVhML8WioZFeoBv+fO+zTzfdZ1pEI6EIchrgxkZ2V9K/PGqgrB+yO5VkMkY1+4MdjmWp/NXuvnHpCODg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T06:04:27.932295Z","bundle_sha256":"082e70950d53c4da13e6cdc156e972adb688e957a6a1c5b2c468880197095b10"}}