{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:JE4CIRGOUGGCI3X5GV7XQPABYS","short_pith_number":"pith:JE4CIRGO","canonical_record":{"source":{"id":"1503.03506","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-11T21:09:28Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d35cb86967452b6e655c6d4f5bcbcbca7d7194f19c3b900143f44d152d2b95cb","abstract_canon_sha256":"32755bcc05cd8835e7c84e24a6af7e9c5685a86ac8cdc4c2a600d5fa5f0be077"},"schema_version":"1.0"},"canonical_sha256":"49382444cea18c246efd357f783c01c4bc1830829baeafa56cc36accc2eaf7d0","source":{"kind":"arxiv","id":"1503.03506","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.03506","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"arxiv_version","alias_value":"1503.03506v1","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.03506","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"pith_short_12","alias_value":"JE4CIRGOUGGC","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"JE4CIRGOUGGCI3X5","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"JE4CIRGO","created_at":"2026-05-18T12:29:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:JE4CIRGOUGGCI3X5GV7XQPABYS","target":"record","payload":{"canonical_record":{"source":{"id":"1503.03506","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-11T21:09:28Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d35cb86967452b6e655c6d4f5bcbcbca7d7194f19c3b900143f44d152d2b95cb","abstract_canon_sha256":"32755bcc05cd8835e7c84e24a6af7e9c5685a86ac8cdc4c2a600d5fa5f0be077"},"schema_version":"1.0"},"canonical_sha256":"49382444cea18c246efd357f783c01c4bc1830829baeafa56cc36accc2eaf7d0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:24:58.396230Z","signature_b64":"arBA7mIASbwGFtCG36XQX8AoCGMF3jv8kYvc8GmBCuLiomDPnJrdyhDjL/u20s/RFBvwe1LNRWIu8N/tqX5kDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49382444cea18c246efd357f783c01c4bc1830829baeafa56cc36accc2eaf7d0","last_reissued_at":"2026-05-18T02:24:58.395576Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:24:58.395576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1503.03506","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":"2026-05-18T02:24:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UJFSeC+n+XR2RxW7plP5jYrIL/tpcpUr/eoO+4X3QbrKnwISDe69RVyk4aevDWxhDOzbg+sCEDTKgKg75pSxAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T21:31:35.408464Z"},"content_sha256":"2a1119fd1a77038339b9c8218d3614534279a9cbb2b44f3c80aae3f071dc9b47","schema_version":"1.0","event_id":"sha256:2a1119fd1a77038339b9c8218d3614534279a9cbb2b44f3c80aae3f071dc9b47"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:JE4CIRGOUGGCI3X5GV7XQPABYS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Christian Wachinger, Polina Golland","submitted_at":"2015-03-11T21:09:28Z","abstract_excerpt":"High computational costs of manifold learning prohibit its application for large point sets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the Nystr\\\"om method. The two main challenges that arise are: (i) the landmarks selected in non-Euclidean geometries must result in a low reconstruction error, (ii) the graph constructed from sparsely sampled landmarks must approximate the manifold well. We propose the sampling of landmarks from determinantal distributions on non-Euclidean spaces. Sin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.03506","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":"2026-05-18T02:24:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IHUxbsLgdl12IjvH/hrwV7SdQILbR4v8c6hWNEM1UGCl6zwOrUk75kQMvJ+QNd8z4LC+CxwmMJYbJe2iwP1PBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T21:31:35.408833Z"},"content_sha256":"9277eb0e78a12dc07398451f9d794b30d1ccc12d0f2058db551fcd30bfd930b1","schema_version":"1.0","event_id":"sha256:9277eb0e78a12dc07398451f9d794b30d1ccc12d0f2058db551fcd30bfd930b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JE4CIRGOUGGCI3X5GV7XQPABYS/bundle.json","state_url":"https://pith.science/pith/JE4CIRGOUGGCI3X5GV7XQPABYS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JE4CIRGOUGGCI3X5GV7XQPABYS/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":"2026-06-04T21:31:35Z","links":{"resolver":"https://pith.science/pith/JE4CIRGOUGGCI3X5GV7XQPABYS","bundle":"https://pith.science/pith/JE4CIRGOUGGCI3X5GV7XQPABYS/bundle.json","state":"https://pith.science/pith/JE4CIRGOUGGCI3X5GV7XQPABYS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JE4CIRGOUGGCI3X5GV7XQPABYS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:JE4CIRGOUGGCI3X5GV7XQPABYS","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":"32755bcc05cd8835e7c84e24a6af7e9c5685a86ac8cdc4c2a600d5fa5f0be077","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-11T21:09:28Z","title_canon_sha256":"d35cb86967452b6e655c6d4f5bcbcbca7d7194f19c3b900143f44d152d2b95cb"},"schema_version":"1.0","source":{"id":"1503.03506","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.03506","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"arxiv_version","alias_value":"1503.03506v1","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.03506","created_at":"2026-05-18T02:24:58Z"},{"alias_kind":"pith_short_12","alias_value":"JE4CIRGOUGGC","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_16","alias_value":"JE4CIRGOUGGCI3X5","created_at":"2026-05-18T12:29:27Z"},{"alias_kind":"pith_short_8","alias_value":"JE4CIRGO","created_at":"2026-05-18T12:29:27Z"}],"graph_snapshots":[{"event_id":"sha256:9277eb0e78a12dc07398451f9d794b30d1ccc12d0f2058db551fcd30bfd930b1","target":"graph","created_at":"2026-05-18T02:24:58Z","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":"High computational costs of manifold learning prohibit its application for large point sets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the Nystr\\\"om method. The two main challenges that arise are: (i) the landmarks selected in non-Euclidean geometries must result in a low reconstruction error, (ii) the graph constructed from sparsely sampled landmarks must approximate the manifold well. We propose the sampling of landmarks from determinantal distributions on non-Euclidean spaces. Sin","authors_text":"Christian Wachinger, Polina Golland","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-11T21:09:28Z","title":"Diverse Landmark Sampling from Determinantal Point Processes for Scalable Manifold Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.03506","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":"sha256:2a1119fd1a77038339b9c8218d3614534279a9cbb2b44f3c80aae3f071dc9b47","target":"record","created_at":"2026-05-18T02:24:58Z","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":"32755bcc05cd8835e7c84e24a6af7e9c5685a86ac8cdc4c2a600d5fa5f0be077","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-03-11T21:09:28Z","title_canon_sha256":"d35cb86967452b6e655c6d4f5bcbcbca7d7194f19c3b900143f44d152d2b95cb"},"schema_version":"1.0","source":{"id":"1503.03506","kind":"arxiv","version":1}},"canonical_sha256":"49382444cea18c246efd357f783c01c4bc1830829baeafa56cc36accc2eaf7d0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"49382444cea18c246efd357f783c01c4bc1830829baeafa56cc36accc2eaf7d0","first_computed_at":"2026-05-18T02:24:58.395576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:24:58.395576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"arBA7mIASbwGFtCG36XQX8AoCGMF3jv8kYvc8GmBCuLiomDPnJrdyhDjL/u20s/RFBvwe1LNRWIu8N/tqX5kDA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:24:58.396230Z","signed_message":"canonical_sha256_bytes"},"source_id":"1503.03506","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a1119fd1a77038339b9c8218d3614534279a9cbb2b44f3c80aae3f071dc9b47","sha256:9277eb0e78a12dc07398451f9d794b30d1ccc12d0f2058db551fcd30bfd930b1"],"state_sha256":"d4d0c98fe978827675303bae20741d53367d4cc51ac34ac457394e22cc5d155c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z/oEwldw+ZnieMCzZGD4yw7F9EpqWDJAAN0URUYudrh8dIQwDnrnkQeSTSQPpwEsgV5FDhhHjAkm9MAf/5ryBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T21:31:35.410946Z","bundle_sha256":"f502bf0d9c1e4c1c8e2949b569ff4a0c592c7cad11f29f3dec3c6ce96a7857a3"}}