{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:XFIUXH7S24SKCGTJ6RPSRMG3UP","short_pith_number":"pith:XFIUXH7S","canonical_record":{"source":{"id":"1506.08170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-26T17:51:57Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"af558f822653fc7dbb55bfb83eee1f45ff6e3f1055473f8394ab6a6f1f0e1f96","abstract_canon_sha256":"53d04e5d73c3863cc4ed3efd66b06ecac2134ed8c62159cb970f6b6f5e0be20f"},"schema_version":"1.0"},"canonical_sha256":"b9514b9ff2d724a11a69f45f28b0dba3e3813e7ce64df79fa3854734dec1aaa3","source":{"kind":"arxiv","id":"1506.08170","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.08170","created_at":"2026-05-18T01:37:47Z"},{"alias_kind":"arxiv_version","alias_value":"1506.08170v1","created_at":"2026-05-18T01:37:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.08170","created_at":"2026-05-18T01:37:47Z"},{"alias_kind":"pith_short_12","alias_value":"XFIUXH7S24SK","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"XFIUXH7S24SKCGTJ","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"XFIUXH7S","created_at":"2026-05-18T12:29:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:XFIUXH7S24SKCGTJ6RPSRMG3UP","target":"record","payload":{"canonical_record":{"source":{"id":"1506.08170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-26T17:51:57Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"af558f822653fc7dbb55bfb83eee1f45ff6e3f1055473f8394ab6a6f1f0e1f96","abstract_canon_sha256":"53d04e5d73c3863cc4ed3efd66b06ecac2134ed8c62159cb970f6b6f5e0be20f"},"schema_version":"1.0"},"canonical_sha256":"b9514b9ff2d724a11a69f45f28b0dba3e3813e7ce64df79fa3854734dec1aaa3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:47.894188Z","signature_b64":"B3ntErNBRB+x/uFkmTgnmdGNbI9oSFJ3Gaxew1kNAW3BvAmTohLQ6kaXrmlikOo++jXg4KEjUj6NRIWiBKICAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9514b9ff2d724a11a69f45f28b0dba3e3813e7ce64df79fa3854734dec1aaa3","last_reissued_at":"2026-05-18T01:37:47.893694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:47.893694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1506.08170","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":"2026-05-18T01:37:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RHw7gCMNAA7ngDf7JFI5l/nFeyx0+jhyIwANIQXK/DMutJW7UNG43YaQvjmkicrPPMf3jFylcz85rFVhbOS+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:05:49.807841Z"},"content_sha256":"b1fb6fb307693ea592cf19cf3f0142d61fa0ab5895fd3131389134029c3c7380","schema_version":"1.0","event_id":"sha256:b1fb6fb307693ea592cf19cf3f0142d61fa0ab5895fd3131389134029c3c7380"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:XFIUXH7S24SKCGTJ6RPSRMG3UP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ML","authors_text":"Dean Foster, Yichao Lu, Zhuang Ma","submitted_at":"2015-06-26T17:51:57Z","abstract_excerpt":"Canonical Correlation Analysis (CCA) is a widely used spectral technique for finding correlation structures in multi-view datasets. In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computationally and storage expensive. We recast CCA from a novel perspective and propose a scalable and memory efficient Augmented Approximate Gradient (AppGrad) scheme for finding top $k$ dimensional canonical subspace which only involves large matrix multiplying a thin matrix of widt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.08170","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":""},"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":"2026-05-18T01:37:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vTFjiHgxJJu8+X1e7/n8OVihxze6Xx6LlKO1qLB6McArbLYg/UWE5wDw/AGd6j8c5SYWDT1H1w+Cgt1FZqaTBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:05:49.808253Z"},"content_sha256":"a5c2b9113e0cb87a5208f8fdd997833ac482f2064513f12bd313806338712e9d","schema_version":"1.0","event_id":"sha256:a5c2b9113e0cb87a5208f8fdd997833ac482f2064513f12bd313806338712e9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XFIUXH7S24SKCGTJ6RPSRMG3UP/bundle.json","state_url":"https://pith.science/pith/XFIUXH7S24SKCGTJ6RPSRMG3UP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XFIUXH7S24SKCGTJ6RPSRMG3UP/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":"2026-05-31T11:05:49Z","links":{"resolver":"https://pith.science/pith/XFIUXH7S24SKCGTJ6RPSRMG3UP","bundle":"https://pith.science/pith/XFIUXH7S24SKCGTJ6RPSRMG3UP/bundle.json","state":"https://pith.science/pith/XFIUXH7S24SKCGTJ6RPSRMG3UP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XFIUXH7S24SKCGTJ6RPSRMG3UP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:XFIUXH7S24SKCGTJ6RPSRMG3UP","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":"53d04e5d73c3863cc4ed3efd66b06ecac2134ed8c62159cb970f6b6f5e0be20f","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-26T17:51:57Z","title_canon_sha256":"af558f822653fc7dbb55bfb83eee1f45ff6e3f1055473f8394ab6a6f1f0e1f96"},"schema_version":"1.0","source":{"id":"1506.08170","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.08170","created_at":"2026-05-18T01:37:47Z"},{"alias_kind":"arxiv_version","alias_value":"1506.08170v1","created_at":"2026-05-18T01:37:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.08170","created_at":"2026-05-18T01:37:47Z"},{"alias_kind":"pith_short_12","alias_value":"XFIUXH7S24SK","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_16","alias_value":"XFIUXH7S24SKCGTJ","created_at":"2026-05-18T12:29:50Z"},{"alias_kind":"pith_short_8","alias_value":"XFIUXH7S","created_at":"2026-05-18T12:29:50Z"}],"graph_snapshots":[{"event_id":"sha256:a5c2b9113e0cb87a5208f8fdd997833ac482f2064513f12bd313806338712e9d","target":"graph","created_at":"2026-05-18T01:37:47Z","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 widely used spectral technique for finding correlation structures in multi-view datasets. In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computationally and storage expensive. We recast CCA from a novel perspective and propose a scalable and memory efficient Augmented Approximate Gradient (AppGrad) scheme for finding top $k$ dimensional canonical subspace which only involves large matrix multiplying a thin matrix of widt","authors_text":"Dean Foster, Yichao Lu, Zhuang Ma","cross_cats":["stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-26T17:51:57Z","title":"Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.08170","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:b1fb6fb307693ea592cf19cf3f0142d61fa0ab5895fd3131389134029c3c7380","target":"record","created_at":"2026-05-18T01:37:47Z","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":"53d04e5d73c3863cc4ed3efd66b06ecac2134ed8c62159cb970f6b6f5e0be20f","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-26T17:51:57Z","title_canon_sha256":"af558f822653fc7dbb55bfb83eee1f45ff6e3f1055473f8394ab6a6f1f0e1f96"},"schema_version":"1.0","source":{"id":"1506.08170","kind":"arxiv","version":1}},"canonical_sha256":"b9514b9ff2d724a11a69f45f28b0dba3e3813e7ce64df79fa3854734dec1aaa3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b9514b9ff2d724a11a69f45f28b0dba3e3813e7ce64df79fa3854734dec1aaa3","first_computed_at":"2026-05-18T01:37:47.893694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:37:47.893694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B3ntErNBRB+x/uFkmTgnmdGNbI9oSFJ3Gaxew1kNAW3BvAmTohLQ6kaXrmlikOo++jXg4KEjUj6NRIWiBKICAA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:37:47.894188Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.08170","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b1fb6fb307693ea592cf19cf3f0142d61fa0ab5895fd3131389134029c3c7380","sha256:a5c2b9113e0cb87a5208f8fdd997833ac482f2064513f12bd313806338712e9d"],"state_sha256":"a9bbfa10a63e164ab7ce77e0feb8f26fba34a7d2da64d8bca81cd6867f4790bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1wUcw9Qkx+sh9SuY2j8zCPJ8JUmcKx42GVH8WTtBvB8ZZkstR3WpixhJKuLipau8x5IO0B3d/O2jQCrLPbb5CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T11:05:49.810984Z","bundle_sha256":"9916190733ec6c56c61b5aacfd084ef24f538181ff767f9d48fd7f2f330e2afe"}}