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Paper Citation Record · LEDGER

Tensor-Train Parameterization for Ultra Dimensionality Reduction

As of 18 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:1908.04924.

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pith.paper-citation-record.v1
1908.04924 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:34:34.132075Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

19 of 19 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation efc95d6c-d9e2-4074-bf98-a3dc13413f8d · outbound

This paper cites Multilinear analysis of image en- sembles: Tensorfaces,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Multilinear analysis of image en- sembles: Tensorfaces,

Reference 1

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Source-reported events for the cited work

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Observation d6f59bbb-ee25-46cc-adf9-3014a34e9546 · outbound

This paper cites Matrix and tensor decomposition in reco mmender systems,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Matrix and tensor decomposition in reco mmender systems,

Reference 2

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Source-reported events for the cited work

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Observation 7c57d84b-a45c-49f9-972b-20e0943295c5 · outbound

This paper cites Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis

Reference 3

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verified exact
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Observation 67825a9f-b910-436e-b6e9-09d02dde584a · outbound

This paper cites Tensorial extensions of i ndepen- dent component analysis for multisubject FMRI analysis,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Tensorial extensions of i ndepen- dent component analysis for multisubject FMRI analysis,

Reference 4

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Source-reported events for the cited work

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Observation 5ffb1ac7-38ec-4976-9f05-972d874784d1 · outbound

This paper cites Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model

Reference 5

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Source-reported events for the cited work

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Observation 57ea38d5-3014-43a0-a2d9-00257689a0d3 · outbound

This paper cites Tensor embedding methods,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Tensor embedding methods,

Reference 6

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Source-reported events for the cited work

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Observation 24f10e8e-dc7f-4842-a169-c482260c2bf0 · outbound

This paper cites Principal Component Analysis with Tensor Train Subspace.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Principal Component Analysis with Tensor Train Subspace

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 2026-08-18T06:34:40.430872+00:00.

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Observation 88bb58a2-1367-4e38-8b1c-e09516f847b3 · outbound

This paper cites Multiple invariants and generalized r ank of a p-way matrix or tensor,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Multiple invariants and generalized r ank of a p-way matrix or tensor,

Reference 8

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c4368c15-3ad8-4722-9ebe-35b12b5afba1 · outbound

This paper cites Implications of factor analysis of three- way matrices for measurement of change,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Implications of factor analysis of three- way matrices for measurement of change,

Reference 9

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Source-reported events for the cited work

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Observation 022e6b50-489c-4140-b414-51bbe2ba0c32 · outbound

This paper cites Tensor-train decomposition,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Tensor-train decomposition,

Reference 10

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Source-reported events for the cited work

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Observation 1ae9f21c-4cee-4c13-8ba0-c74c31a2fc23 · outbound

This paper cites Liii. on lines and planes of closest fit to sy stems of points in space,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Liii. on lines and planes of closest fit to sy stems of points in space,

Reference 11

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Source-reported events for the cited work

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Observation b9067bdc-2195-4dba-b27d-fbdae4a58fc2 · outbound

This paper cites Locality preserving projections,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Locality preserving projections,

Reference 12

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Source-reported events for the cited work

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Observation 9f20bc27-e9ce-419f-a361-c0b7e344aa28 · outbound

This paper cites Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions,

Reference 13

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verified fuzzy
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Source-reported events for the cited work

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Observation 88385767-66ee-4645-ba2c-b193d1371c5e · outbound

This paper cites Cichocki, N.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Cichocki, N

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c77e0d47-8f29-4db1-acee-f200ce6f4bac · outbound

This paper cites Breaking the curse o f dimensionality, or how to use SVD in many dimensions,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Breaking the curse o f dimensionality, or how to use SVD in many dimensions,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 18233f8c-6fe2-4057-a53d-7b3d47558a10 · outbound

This paper cites Lpp solution schemes for use with face recognition,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Lpp solution schemes for use with face recognition,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 4a762570-423f-4995-8c1d-35797b611920 · outbound

This paper cites Tensor train neighb orhood pre- serving embedding,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction Tensor train neighb orhood pre- serving embedding,

Reference 17

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation ed75609f-3351-440f-9fe9-647bd4150d29 · outbound

This paper cites From fe w to many: Illumination cone models for face recognition under variab le lighting and pose,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction From fe w to many: Illumination cone models for face recognition under variab le lighting and pose,

Reference 18

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Observation 9cd163b7-e4e7-4569-b727-39ddde7ef0cc · outbound

This paper cites 220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3,.

Tensor-Train Parameterization for Ultra Dimensionality Reduction 220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

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