{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:3JMISGDPWEY6SZMOBPTO76F5PF","short_pith_number":"pith:3JMISGDP","canonical_record":{"source":{"id":"1503.06858","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-23T22:00:51Z","cross_cats_sorted":[],"title_canon_sha256":"1cb612e0855eb6d1687a91f02c24eed9a6d77c93b93450ee037616c031dad869","abstract_canon_sha256":"6f0c7b0db3dd3666225e44e436129da913747a2f6e4816c867ed6b04bf93645f"},"schema_version":"1.0"},"canonical_sha256":"da5889186fb131e9658e0be6eff8bd79742d2a6de55dfafe455e4c22b95c10f7","source":{"kind":"arxiv","id":"1503.06858","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.06858","created_at":"2026-05-18T01:20:53Z"},{"alias_kind":"arxiv_version","alias_value":"1503.06858v4","created_at":"2026-05-18T01:20:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.06858","created_at":"2026-05-18T01:20:53Z"},{"alias_kind":"pith_short_12","alias_value":"3JMISGDPWEY6","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"3JMISGDPWEY6SZMO","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"3JMISGDP","created_at":"2026-05-18T12:29:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:3JMISGDPWEY6SZMOBPTO76F5PF","target":"record","payload":{"canonical_record":{"source":{"id":"1503.06858","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-23T22:00:51Z","cross_cats_sorted":[],"title_canon_sha256":"1cb612e0855eb6d1687a91f02c24eed9a6d77c93b93450ee037616c031dad869","abstract_canon_sha256":"6f0c7b0db3dd3666225e44e436129da913747a2f6e4816c867ed6b04bf93645f"},"schema_version":"1.0"},"canonical_sha256":"da5889186fb131e9658e0be6eff8bd79742d2a6de55dfafe455e4c22b95c10f7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:53.897823Z","signature_b64":"U4nTNxNIQ/PKfoiWTKbt1+Dcs8dYigTu5XSObGl98TRnTaZR3ANG7gL52Z4+7On4e0gPnVeZc+8C7jLA40ZBBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da5889186fb131e9658e0be6eff8bd79742d2a6de55dfafe455e4c22b95c10f7","last_reissued_at":"2026-05-18T01:20:53.897125Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:53.897125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1503.06858","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":"2026-05-18T01:20:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Oc77ewz8xTpCgBmcTgbEjM9a7LZtmRDKcx75zOVUID0jwdDQsf+zg/RGq/AX0yiAT8JWsKisbf9bQaQeonw8Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T04:44:49.904508Z"},"content_sha256":"72e6fe5b77e26f1b11efd59c6dfddfc35756fa0e6ffae6c3515651e68b4334fb","schema_version":"1.0","event_id":"sha256:72e6fe5b77e26f1b11efd59c6dfddfc35756fa0e6ffae6c3515651e68b4334fb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:3JMISGDPWEY6SZMOBPTO76F5PF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Communication Efficient Distributed Kernel Principal Component Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bo Xie, David Woodruff, Le Song, Maria-Florina Balcan, Yingyu Liang","submitted_at":"2015-03-23T22:00:51Z","abstract_excerpt":"Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very costly to communicate all of this data to a single data center and then perform kernel PCA. Can we perform kernel PCA on the entire dataset in a distributed and communication efficient fashion while maintaining provable and strong guarantees in solution quality?\n  In this paper, we give an affirmative answer to the question by developing a communication efficient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.06858","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":"2026-05-18T01:20:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B/G4T5zX2b8+23WEZGWW1RHs2munMgvY9M72XMNXlq2Hx0hVSTtZvlt0ip4ptOVEQJ6z6W6bR1GuE27HHwiwAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T04:44:49.904906Z"},"content_sha256":"6c196438683c0f20a358f84421ca22ae872386af672a72a96f47719107ec223b","schema_version":"1.0","event_id":"sha256:6c196438683c0f20a358f84421ca22ae872386af672a72a96f47719107ec223b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3JMISGDPWEY6SZMOBPTO76F5PF/bundle.json","state_url":"https://pith.science/pith/3JMISGDPWEY6SZMOBPTO76F5PF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3JMISGDPWEY6SZMOBPTO76F5PF/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":"2026-06-04T04:44:49Z","links":{"resolver":"https://pith.science/pith/3JMISGDPWEY6SZMOBPTO76F5PF","bundle":"https://pith.science/pith/3JMISGDPWEY6SZMOBPTO76F5PF/bundle.json","state":"https://pith.science/pith/3JMISGDPWEY6SZMOBPTO76F5PF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3JMISGDPWEY6SZMOBPTO76F5PF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:3JMISGDPWEY6SZMOBPTO76F5PF","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":"6f0c7b0db3dd3666225e44e436129da913747a2f6e4816c867ed6b04bf93645f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-23T22:00:51Z","title_canon_sha256":"1cb612e0855eb6d1687a91f02c24eed9a6d77c93b93450ee037616c031dad869"},"schema_version":"1.0","source":{"id":"1503.06858","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.06858","created_at":"2026-05-18T01:20:53Z"},{"alias_kind":"arxiv_version","alias_value":"1503.06858v4","created_at":"2026-05-18T01:20:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.06858","created_at":"2026-05-18T01:20:53Z"},{"alias_kind":"pith_short_12","alias_value":"3JMISGDPWEY6","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"3JMISGDPWEY6SZMO","created_at":"2026-05-18T12:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"3JMISGDP","created_at":"2026-05-18T12:29:02Z"}],"graph_snapshots":[{"event_id":"sha256:6c196438683c0f20a358f84421ca22ae872386af672a72a96f47719107ec223b","target":"graph","created_at":"2026-05-18T01:20:53Z","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":"Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very costly to communicate all of this data to a single data center and then perform kernel PCA. Can we perform kernel PCA on the entire dataset in a distributed and communication efficient fashion while maintaining provable and strong guarantees in solution quality?\n  In this paper, we give an affirmative answer to the question by developing a communication efficient","authors_text":"Bo Xie, David Woodruff, Le Song, Maria-Florina Balcan, Yingyu Liang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-23T22:00:51Z","title":"Communication Efficient Distributed Kernel Principal Component Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.06858","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":"sha256:72e6fe5b77e26f1b11efd59c6dfddfc35756fa0e6ffae6c3515651e68b4334fb","target":"record","created_at":"2026-05-18T01:20:53Z","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":"6f0c7b0db3dd3666225e44e436129da913747a2f6e4816c867ed6b04bf93645f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-23T22:00:51Z","title_canon_sha256":"1cb612e0855eb6d1687a91f02c24eed9a6d77c93b93450ee037616c031dad869"},"schema_version":"1.0","source":{"id":"1503.06858","kind":"arxiv","version":4}},"canonical_sha256":"da5889186fb131e9658e0be6eff8bd79742d2a6de55dfafe455e4c22b95c10f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da5889186fb131e9658e0be6eff8bd79742d2a6de55dfafe455e4c22b95c10f7","first_computed_at":"2026-05-18T01:20:53.897125Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:53.897125Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U4nTNxNIQ/PKfoiWTKbt1+Dcs8dYigTu5XSObGl98TRnTaZR3ANG7gL52Z4+7On4e0gPnVeZc+8C7jLA40ZBBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:53.897823Z","signed_message":"canonical_sha256_bytes"},"source_id":"1503.06858","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72e6fe5b77e26f1b11efd59c6dfddfc35756fa0e6ffae6c3515651e68b4334fb","sha256:6c196438683c0f20a358f84421ca22ae872386af672a72a96f47719107ec223b"],"state_sha256":"00c1152add4aa50263ca5b107fe15f84e84dd397177d13d3ac1ff6e5ddb90d2d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"otQpO+sqMzT8oGaN1c9yHQAda7pHoWOqjmoDD8SpF7Cov4O+y5hvDgJQl+CHDZOvMlxA/Z2Re8Zz2X7LA8eFDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T04:44:49.907670Z","bundle_sha256":"090a40f15e12c8f40426e4168da0c117f4e61e166584e2f496485ed7d53d118b"}}