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Paper Citation Record · LEDGER

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation

As of 11 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.08535.

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

pith.paper-citation-record.v1
2506.08535 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:14:34.060679Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

25 of 25 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4e90303-9d19-43bd-8d73-824bdd835fc4 · outbound

This paper cites A maximally split and adaptive relaxed al- ternating direction method of multipliers for regularized extreme learning machines,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation A maximally split and adaptive relaxed al- ternating direction method of multipliers for regularized extreme learning machines,

Reference 1

Resolution
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Observation 99ea13fe-3b13-4c06-9e2a-920bfdaf137b · outbound

This paper cites Sparse pattern selection strategies for robust frobenius-norm minimization preconditioners in electromagnetism,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Sparse pattern selection strategies for robust frobenius-norm minimization preconditioners in electromagnetism,

Reference 2

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Observation b25d1bca-b994-4c5a-a817-a08ad7e9186c · outbound

This paper cites Generalized matrix spectral factorization with symmetry and construction of quasi-tight framelets over algebraic number fields,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Generalized matrix spectral factorization with symmetry and construction of quasi-tight framelets over algebraic number fields,

Reference 3

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Observation f8429e6c-91a3-45a1-a2bf-3319704f65a4 · outbound

This paper cites A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis,

Reference 4

Resolution
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Observation db164bd6-d6b8-4528-a1a1-900331483c33 · outbound

This paper cites an unresolved cited work.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Unresolved cited work

Reference 5

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Source-reported events for the cited work

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Observation 5ff6d4fb-7d16-4cd8-8c81-5565c688fdfd · outbound

This paper cites Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization,

Reference 6

Resolution
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Observation 61d08d47-257a-4e84-a039-ab74111a5c11 · outbound

This paper cites A Validation Approach to Over-parameterized Matrix and Image Recovery.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation A Validation Approach to Over-parameterized Matrix and Image Recovery

Reference 7

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 823296e4-d88c-47bb-9150-51caf8d4c3a8 · outbound

This paper cites Why are big data matrices approximately low rank?.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Why are big data matrices approximately low rank?

Reference 8

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Source-reported events for the cited work

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Observation 635c667d-1675-43c1-b3d7-203e484f9736 · outbound

This paper cites Rank-revealing qr factorizations and the singular value decomposition,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Rank-revealing qr factorizations and the singular value decomposition,

Reference 9

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Observation 65ddf9d7-f0d7-414c-bee6-f972850e38e6 · outbound

This paper cites Guaranteed minimum-rank solutions of linear matrix equa- tions via nuclear norm minimization,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Guaranteed minimum-rank solutions of linear matrix equa- tions via nuclear norm minimization,

Reference 10

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Observation 79bfb666-2134-4daf-8b12-6e88d92a5126 · outbound

This paper cites A deterministic cur matrix decomposition algorithm for improved large-scale data analysis,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation A deterministic cur matrix decomposition algorithm for improved large-scale data analysis,

Reference 11

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Observation 982acb22-c308-439d-a750-4efc31b84209 · outbound

This paper cites Sparse non-negative matrix factorizations via alternating non-negativity- constrained least squares for microarray data analysis,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Sparse non-negative matrix factorizations via alternating non-negativity- constrained least squares for microarray data analysis,

Reference 12

Resolution
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Observation af197f7f-6857-4e74-8d0c-ac26f608e851 · outbound

This paper cites Cur matrix decompositions for improved data analysis,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Cur matrix decompositions for improved data analysis,

Reference 13

Resolution
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Observation 0c4c28f1-1ce1-434c-b31a-c4f3c98b3658 · outbound

This paper cites A regularized matrix factorization approach to induce structured sparse–low–rank solutions in the eeg inverse problem,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation A regularized matrix factorization approach to induce structured sparse–low–rank solutions in the eeg inverse problem,

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 38a4dd3a-f809-484c-a74a-ff199ed01b0a · outbound

This paper cites Matrix factorization techniques in machine learning, signal processing, and statistics,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Matrix factorization techniques in machine learning, signal processing, and statistics,

Reference 15

Resolution
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Source-reported events for the cited work

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Observation be996716-2598-4d8b-a904-4bd85d32c691 · outbound

This paper cites Sparse principal component analysis,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Sparse principal component analysis,

Reference 16

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Source-reported events for the cited work

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Observation 0e6238d4-eeb5-4225-9097-66851beab867 · outbound

This paper cites General tensor decomposition, moment matrices and applications,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation General tensor decomposition, moment matrices and applications,

Reference 17

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Source-reported events for the cited work

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Observation db8f9ad8-1d81-49ad-96d9-3e2272f9bf27 · outbound

This paper cites Low-rank matrix recovery via iteratively reweighted least squares min- imization,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Low-rank matrix recovery via iteratively reweighted least squares min- imization,

Reference 18

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verified fuzzy
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Source-reported events for the cited work

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Observation 4c007f05-5b75-4ae2-aae1-f010f3e741bc · outbound

This paper cites A framework of regularized low-rank matrix models for regression and classification,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation A framework of regularized low-rank matrix models for regression and classification,

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 5bdf7af7-cf25-48a7-b0a3-53d34cb958cb · outbound

This paper cites Accelerating nuclear-norm regularized low-rank matrix op- timization through burer–monteiro decomposition,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Accelerating nuclear-norm regularized low-rank matrix op- timization through burer–monteiro decomposition,

Reference 20

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Source-reported events for the cited work

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Observation 960129cd-1860-4854-84d3-347ed1742677 · outbound

This paper cites Deep matrix factorization with noise-adaptive regularization,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Deep matrix factorization with noise-adaptive regularization,

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 85abc66f-8cbc-497b-a9f1-128bde7cc7c9 · outbound

This paper cites Variational bayesian matrix factorization with spectral priors,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Variational bayesian matrix factorization with spectral priors,

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation fa1e0ae9-679c-4cf1-b048-59e5bb1d24d2 · outbound

This paper cites Neural parameterized low-rank models for structured matrix approx- imation,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Neural parameterized low-rank models for structured matrix approx- imation,

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation ed991e79-281b-4dd8-8ebf-d291d2fbbbf8 · outbound

This paper cites Bayesian nonparametric low-rank matrix estimation with hierar- chical priors,.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Bayesian nonparametric low-rank matrix estimation with hierar- chical priors,

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 45b4d7e9-3674-4d78-bea6-25bc3ed6e04b · outbound

This paper cites Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models.

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models

Reference 25

Resolution
verified exact
local_arxiv, observed 2026-08-07T05:14:34.104980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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