{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:6V5ZMSRIVUFW3WVQMYAWPDMID4","short_pith_number":"pith:6V5ZMSRI","schema_version":"1.0","canonical_sha256":"f57b964a28ad0b6ddab06601678d881f26a473491dac797039c71d4f20a41a2e","source":{"kind":"arxiv","id":"1512.05059","version":1},"attestation_state":"computed","paper":{"title":"Streaming Kernel Principal Component Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.DS","authors_text":"Daniel Perry, Jeff M. Phillips, Mina Ghashami","submitted_at":"2015-12-16T06:12:55Z","abstract_excerpt":"Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \\times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time for large values of $n$. Techniques such as the Nystr\\\"om method and random feature maps can help towards this goal, but they do not explicitly maintain the basis vectors in a stream and take more space than desired. We propose a new approach for"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1512.05059","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2015-12-16T06:12:55Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"c912aca325a78df6c5dddef7dfc2ef959e3641235c03408096dc7ec57f89d976","abstract_canon_sha256":"f9b682ac1881ee0ad05d987da9e028e71f4f2d28d84c9f0804d0971864c795af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:13.608029Z","signature_b64":"WVTjEcX9dTORVFGjs5aVC0olrlHBjEgrkQxr2dZqUjfxlmPzYOIgTUh59I9teG9s/eI95AkfAtrD8x42xp+oCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f57b964a28ad0b6ddab06601678d881f26a473491dac797039c71d4f20a41a2e","last_reissued_at":"2026-05-18T01:24:13.607632Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:13.607632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Streaming Kernel Principal Component Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.DS","authors_text":"Daniel Perry, Jeff M. Phillips, Mina Ghashami","submitted_at":"2015-12-16T06:12:55Z","abstract_excerpt":"Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \\times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time for large values of $n$. Techniques such as the Nystr\\\"om method and random feature maps can help towards this goal, but they do not explicitly maintain the basis vectors in a stream and take more space than desired. We propose a new approach for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.05059","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1512.05059","created_at":"2026-05-18T01:24:13.607691+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.05059v1","created_at":"2026-05-18T01:24:13.607691+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.05059","created_at":"2026-05-18T01:24:13.607691+00:00"},{"alias_kind":"pith_short_12","alias_value":"6V5ZMSRIVUFW","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_16","alias_value":"6V5ZMSRIVUFW3WVQ","created_at":"2026-05-18T12:29:07.941421+00:00"},{"alias_kind":"pith_short_8","alias_value":"6V5ZMSRI","created_at":"2026-05-18T12:29:07.941421+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4","json":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4.json","graph_json":"https://pith.science/api/pith-number/6V5ZMSRIVUFW3WVQMYAWPDMID4/graph.json","events_json":"https://pith.science/api/pith-number/6V5ZMSRIVUFW3WVQMYAWPDMID4/events.json","paper":"https://pith.science/paper/6V5ZMSRI"},"agent_actions":{"view_html":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4","download_json":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4.json","view_paper":"https://pith.science/paper/6V5ZMSRI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.05059&json=true","fetch_graph":"https://pith.science/api/pith-number/6V5ZMSRIVUFW3WVQMYAWPDMID4/graph.json","fetch_events":"https://pith.science/api/pith-number/6V5ZMSRIVUFW3WVQMYAWPDMID4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4/action/storage_attestation","attest_author":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4/action/author_attestation","sign_citation":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4/action/citation_signature","submit_replication":"https://pith.science/pith/6V5ZMSRIVUFW3WVQMYAWPDMID4/action/replication_record"}},"created_at":"2026-05-18T01:24:13.607691+00:00","updated_at":"2026-05-18T01:24:13.607691+00:00"}