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Paper Citation Record · LEDGER

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting

As of 12 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2512.02866.

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

pith.paper-citation-record.v1
2512.02866 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:02:28.890312Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T23:11:05.443406Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

12 of 12 outbound references displayed

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  • verified fuzzy0
  • unresolved12
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation ac14b4cc-0dba-4e3f-9731-9972fe86d83d · outbound

This paper cites In particular, the top eigen- pair (λ max(B(w)), vmax(w)) isC 1 on Ω, which impliesθ(w) = 1−λ max(B(w)) is also C1.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting In particular, the top eigen- pair (λ max(B(w)), vmax(w)) isC 1 on Ω, which impliesθ(w) = 1−λ max(B(w)) is also C1

Reference 1

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unresolved
no resolver link, observed 2026-08-03T19:02:28.476611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:28.476611Z digest=sha256:568ccdc64f01c030df607da2a30c9151a6d878f627712d277b8e68440e702557

Observation bcc325e8-0510-483b-953a-272abdf42963 · outbound

This paper cites D1 0 D3 D2 #.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting D1 0 D3 D2 #

Reference 2

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no resolver link, observed 2026-08-03T19:02:28.260049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:28.260049Z digest=sha256:ee85d441c94e559a95de45d9620c5424a5708125782dcee302ec21b597111322

Observation 5064d6ac-569c-4aea-b639-57800c19813a · outbound

This paper cites Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices

Reference 3

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unresolved
no resolver link, observed 2026-08-03T19:02:27.783190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:27.783190Z digest=sha256:325b539169abdc71a142c8e9ff989441271121bc3eeabe3bc5c047c491f6c18b

Observation e6e7d2e4-f789-4a0f-9afb-09bbc27cd34b · outbound

This paper cites V ⊤ k W ⊤ k # h V k W k i , and also noticeU ⊤ ¯U k = h I0 i , and ¯U ⊤ k U ⊥ =.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting V ⊤ k W ⊤ k # h V k W k i , and also noticeU ⊤ ¯U k = h I0 i , and ¯U ⊤ k U ⊥ =

Reference 5

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no resolver link, observed 2026-08-03T19:02:28.105970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:28.105970Z digest=sha256:5056ac370e962fde9983ce539e4bd1232e54fda229ac4383a36cf2f7f00820bc

Observation 6e6f8239-1f52-4793-a479-b2a52e18cfc3 · outbound

This paper cites Gk1 Gk2 H k1 H k2 # =.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting Gk1 Gk2 H k1 H k2 # =

Reference 11

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unresolved
no resolver link, observed 2026-08-03T19:02:28.679255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:28.679255Z digest=sha256:0899ffbcd784b06741f187812be97377412fa60e51fb3359376719e589013021

Observation 7b66be45-b9b9-4665-95a6-a0ca729ad001 · outbound

This paper cites Lemma 14.LetX∈R m×n be a random matrix whose entriesX ij are independent mean- zero sub-gaussian random variables.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting Lemma 14.LetX∈R m×n be a random matrix whose entriesX ij are independent mean- zero sub-gaussian random variables

Reference 12

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unresolved
no resolver link, observed 2026-08-03T19:02:28.890312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:28.890312Z digest=sha256:1f735d2013e53c63aac7400fa8c588c7faf325789a77582cf73c7654decbbed6

Observation 31bc0119-4ddc-4156-b507-500796ba333b · outbound

This paper cites A spectral method for multi-view subspace learning using the product of projections.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting A spectral method for multi-view subspace learning using the product of projections

Reference 1976

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unresolved
no resolver link, observed 2026-08-03T19:02:27.889454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:27.889454Z digest=sha256:1ace9fe5ee12bf166f06b04cd8ef576f2ff7615a62dfeb4dfca99292794584f7

Observation 43c6e754-da5f-4c6f-914b-bbc591a90e1e · outbound

This paper cites Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm

Reference 2013

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no resolver link, observed 2026-08-03T19:02:27.699052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:27.699052Z digest=sha256:c1809fc7cd972212f0b735a4e74a1a5d9ee1625b4520308fa824458786110401

Observation 290595b3-1f1a-4e5c-9c30-64a3a6098c1e · outbound

This paper cites HeteroJIVE: Joint Subspace Estimation for Heterogeneous Multi-View Data.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting HeteroJIVE: Joint Subspace Estimation for Heterogeneous Multi-View Data

Reference 2015

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unresolved
no resolver link, observed 2026-08-03T19:02:28.082169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:28.082169Z digest=sha256:647c871404b39dd9af1b6fa92b92d573813880e3506c4ac79cd6b65c48977348

Observation 26e6df62-8a21-405b-a739-fa21d82f2e21 · outbound

This paper cites Stacked SVD or SVD stacked? A Random Matrix Theory perspective on data integration.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting Stacked SVD or SVD stacked? A Random Matrix Theory perspective on data integration

Reference 2021

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unresolved
no resolver link, observed 2026-08-03T19:02:27.653620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:27.653620Z digest=sha256:e52e966b384cd3b2991d1947c988340a605cdd040b37b639cad65493839780e7

Observation b987e8a0-bda1-46ea-a439-646f2a0b19dc · outbound

This paper cites Limit results for distributed estimation of invariant sub- spaces in multiple networks inference and pca.arXiv preprint arXiv:2206.04306,.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting Limit results for distributed estimation of invariant sub- spaces in multiple networks inference and pca.arXiv preprint arXiv:2206.04306,

Reference 2022

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unresolved
no resolver link, observed 2026-08-03T19:02:28.057244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:28.057244Z digest=sha256:9df900dec3224775716f718a2b4bd8bac48853ce14c204a3964043e227c0016b

Observation 83082cdc-f666-43c5-b074-a7904a7239fe · outbound

This paper cites Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices.

Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices

Reference 2024

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unresolved
no resolver link, observed 2026-08-03T19:02:27.962581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:02:27.962581Z digest=sha256:17b28f40bf5141e51ee718fba13df4b22f9a2b1a4b6dd212438e90eeb54090f0

Pith citing papers

Observation b3e1a42a-5c1a-4a4d-ac10-97133e687e6a · inbound

Statistically and Computationally Optimal Estimation and Inference of Common Subspaces cites this paper.

Statistically and Computationally Optimal Estimation and Inference of Common Subspaces Spectral Joint Subspace Estimation for Heterogeneous Multi-View Data: Geometry and Reweighting

Reference 105

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verified exact
arxiv_id, observed 2026-08-03T02:10:59.504313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-06-27T23:11:05.443406Z digest=sha256:5b50a4896c355608097df950326646feb358beae10bc86a0dcd711972bccfd7a