Paper Citation Record · LEDGER

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques

As of 9 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2506.16663.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.16663 v3

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:41:20.904491Z

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

7 of 7 outbound references displayed

verified exact1
verified fuzzy3
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5c01cae-b43c-484f-8a24-d9592825aa79 · outbound

This paper cites On Lines and Planes of Closest Fit to Systems of Points in Space,.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques On Lines and Planes of Closest Fit to Systems of Points in Space,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:21.873434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3c90511d-0f0b-4018-b814-cf9f56e02b54 · outbound

This paper cites The Approxima- tion of One Matrix by Another of Lower Rank,.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques The Approxima- tion of One Matrix by Another of Lower Rank,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:21.855710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3ec1fa1a-79f4-4f8a-aed1-5c1de162f972 · outbound

This paper cites Singular Value Decomposition and Least Squares So- lutions,.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques Singular Value Decomposition and Least Squares So- lutions,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:41:21.798454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 80141c1d-b472-469a-9d8f-3468f4b95a18 · outbound

This paper cites Principal Com- ponent Analysis: A Review and Recent De- velopments,.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques Principal Com- ponent Analysis: A Review and Recent De- velopments,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:20.472444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62d13b06-58fb-4083-aa83-50b7c7a0cd27 · outbound

This paper cites QRPCA: A Pack- age for Fast Principal Component Anal- ysis with GPU Acceleration,.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques QRPCA: A Pack- age for Fast Principal Component Anal- ysis with GPU Acceleration,

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T23:41:21.528505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 64812ccd-1929-4ced-bd92-1c66bd1ececb · outbound

This paper cites Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques Randomized algorithms for low-rank matrix approximation: Design, analysis, and applications

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:41:20.731323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73c8bcd6-a1e1-4e5d-9105-019aadfc6b6c · outbound

This paper cites KPCA-CAM: Visual Explainability of Deep Computer Vision Models Using Kernel PCA,.

A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques KPCA-CAM: Visual Explainability of Deep Computer Vision Models Using Kernel PCA,

Reference 7

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:41:21.268226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

No inbound Pith citation observations are available.