Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:41:20.904491Z
Paper Citation Record · LEDGER
A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques
As of 18 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.