{"as_of":"2026-08-10T15:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e423e40a6a666fa592513f24d230dcbf7039c1a37ec6c762dc4e15e64b730ade","coverage":[{"denominator":7,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:41:20.904491Z","state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.16663/citation-record","integrity":"/paper/2506.16663/integrity","json":"/paper/2506.16663/citation-record.json","paper":"/paper/2506.16663"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:41:21.867743Z","title":"On Lines and Planes of Closest Fit to Systems of Points in Space,","venue":null,"work_id":"e409e49d-ebac-471c-85c2-6fe458cc82c0","year":1901},"citing_paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:20.079996Z"},"links":{"citing_paper":"/paper/2506.16663"},"observation_digest":"sha256:994f1f3d904e3635cd4357c096bd36e833a77a8a885a037c254f196655e59aec","observation_id":"c5c01cae-b43c-484f-8a24-d9592825aa79","resolution":{"observed_at":"2026-08-06T23:41:21.873434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:41:21.849922Z","title":"The Approxima- tion of One Matrix by Another of Lower Rank,","venue":null,"work_id":"43a3d1e2-a4b6-44f7-a63f-9821c467b3d0","year":1936},"citing_paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:20.190381Z"},"links":{"citing_paper":"/paper/2506.16663"},"observation_digest":"sha256:ae0d444cf2931eace7c767ca99392ea16470d3f6fea82ea0f4edf6d1fc3722cd","observation_id":"3c90511d-0f0b-4018-b814-cf9f56e02b54","resolution":{"observed_at":"2026-08-06T23:41:21.855710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:41:21.749020Z","title":"Singular Value Decomposition and Least Squares So- lutions,","venue":null,"work_id":"ea2f1df7-1b4a-4070-84e3-14c9f6643728","year":1970},"citing_paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:20.352944Z"},"links":{"citing_paper":"/paper/2506.16663"},"observation_digest":"sha256:11e5d34d066ea53834d77bb41daee17eca3d685571d7118a1819c472c6774132","observation_id":"3ec1fa1a-79f4-4f8a-aed1-5c1de162f972","resolution":{"observed_at":"2026-08-06T23:41:21.798454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:41:20.472444Z","title":"Principal Com- ponent Analysis: A Review and Recent De- velopments,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:20.472444Z"},"links":{"citing_paper":"/paper/2506.16663"},"observation_digest":"sha256:93da7936c78b719ce679d90ca8b9399354414143ec22fae9d03766aa98ebf3c9","observation_id":"80141c1d-b472-469a-9d8f-3468f4b95a18","resolution":{"observed_at":"2026-08-06T23:41:20.472444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.10063","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:41:21.453316Z","title":"QRPCA: A Pack- age for Fast Principal Component Anal- ysis with GPU Acceleration,","venue":null,"work_id":"aff8f941-1584-49d7-be77-eb4c5dd3f9c6","year":2025},"citing_paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:20.590259Z"},"links":{"citing_paper":"/paper/2506.16663"},"observation_digest":"sha256:43d980c04e2a107ca9fe5a5de9937bdac25a195eafaa1a1113b17f384200473e","observation_id":"62d13b06-58fb-4083-aa83-50b7c7a0cd27","resolution":{"observed_at":"2026-08-06T23:41:21.528505Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.12418","last_updated":"2023-09-22T02:57:04Z","snapshot_observed_at":"2026-07-06T15:45:13.905037Z","submitted_at":"2023-06-21T17:57:57Z","title":"Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.12418","snapshot_observed_at":"2026-08-06T23:41:20.731323Z","title":"Randomized Algorithms for Low-Rank Matrix Approxi- mation: Design, Analysis, and Applications,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:20.731323Z"},"links":{"cited_paper":"/paper/2306.12418","citing_paper":"/paper/2506.16663"},"observation_digest":"sha256:7ab564dfbb08100cde2c39eed6034bf70fb5d15ae96a9bbcf13e83df500e2e29","observation_id":"64812ccd-1929-4ced-bd92-1c66bd1ececb","resolution":{"observed_at":"2026-08-06T23:41:20.731323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"9620.2024","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:41:21.145945Z","title":"KPCA-CAM: Visual Explainability of Deep Computer Vision Models Using Kernel PCA,","venue":null,"work_id":"82cb4e9b-e6c4-4193-9ec5-52ce724bc22e","year":2025},"citing_paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:41:20.904491Z"},"links":{"citing_paper":"/paper/2506.16663"},"observation_digest":"sha256:9d4cb386118c15fb258850e1db21a5a30d3582180440cf875c5af858e9304dd6","observation_id":"73c8bcd6-a1e1-4e5d-9105-019aadfc6b6c","resolution":{"observed_at":"2026-08-06T23:41:21.268226Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.16663","last_updated":"2025-06-25T18:39:32Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T13:19:18.633220Z","submitted_at":"2025-06-20T00:19:45Z","title":"A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques"},"reference_resolution":{"displayed":7,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":2,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":7},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2506.16663."}