On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery | Paper Citation Record

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This paper cites The approximation of one matrix by another of lower rank.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery The approximation of one matrix by another of lower rank

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This paper cites Angen¨ aherte Aufl¨ osung von Systemen linearer Gleichungen.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Angen¨ aherte Aufl¨ osung von Systemen linearer Gleichungen

Reference 2

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This paper cites A multilinear singular value decomposition.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery A multilinear singular value decomposition

Reference 3

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This paper cites On the best rank-1 and rank-(r1, r2,..., rn) approximation of higher-order tensors.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery On the best rank-1 and rank-(r1, r2,..., rn) approximation of higher-order tensors

Reference 4

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This paper cites Construction and analysis of degenerate PARAF AC models.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Construction and analysis of degenerate PARAF AC models

Reference 5

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This paper cites Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information

Reference 6

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This paper cites The restricted isometry property and its implications for compressed sensing.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery The restricted isometry property and its implications for compressed sensing

Reference 7

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This paper cites The fast Johnson–Lindenstrauss transform and approximate nearest neighbors.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery The fast Johnson–Lindenstrauss transform and approximate nearest neighbors

Reference 8

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This paper cites Tensor decompositions and applications.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensor decompositions and applications

Reference 9

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This paper cites A randomized Kaczmarz algorithm with expo- nential convergence.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery A randomized Kaczmarz algorithm with expo- nential convergence

Reference 10

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This paper cites A singular value thresholding al- gorithm for matrix completion.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery A singular value thresholding al- gorithm for matrix completion

Reference 11

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This paper cites A sparse Johnson- Lindenstrauss trans- form.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery A sparse Johnson- Lindenstrauss trans- form

Reference 12

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This paper cites Tensor completion for estimating missing values in visual data.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensor completion for estimating missing values in visual data

Reference 13

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This paper cites A new truncation strategy for the higher-order singular value decomposition.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery A new truncation strategy for the higher-order singular value decomposition

Reference 14

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This paper cites Normalized iterative hard thresholding for matrix completion.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Normalized iterative hard thresholding for matrix completion

Reference 15

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This paper cites Sparser Johnson- Lindenstrauss transforms.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Sparser Johnson- Lindenstrauss transforms

Reference 16

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This paper cites ROP: Matrix recovery via rank-one projections.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery ROP: Matrix recovery via rank-one projections

Reference 17

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This paper cites Tensor decompositions for signal processing applications: From two- way to multiway component analysis.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensor decompositions for signal processing applications: From two- way to multiway component analysis

Reference 18

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This paper cites Dimensionality reduction for k-means clustering and low rank ap- proximation.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Dimensionality reduction for k-means clustering and low rank ap- proximation

Reference 19

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This paper cites An iterative hard thresholding algorithm with improved convergence for low-rank tensor recovery.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery An iterative hard thresholding algorithm with improved convergence for low-rank tensor recovery

Reference 20

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This paper cites Modified distributed iterative hard thresh- olding.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Modified distributed iterative hard thresh- olding

Reference 21

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This paper cites Iterative hard thresholding based on ran- domized Kaczmarz method.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Iterative hard thresholding based on ran- domized Kaczmarz method

Reference 22

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This paper cites Efficient matrix sensing using rank-1 gauss- ian measurements.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Efficient matrix sensing using rank-1 gauss- ian measurements

Reference 23

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This paper cites Parallel tensor compression for large- scale scientific data.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Parallel tensor compression for large- scale scientific data

Reference 24

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On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Kaczmarz method for solving quadratic equations

Reference 25

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This paper cites Conjugate gradient acceleration of iteratively re-weighted least squares methods.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Conjugate gradient acceleration of iteratively re-weighted least squares methods

Reference 26

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This paper cites Support tensor machines for classification of hyperspectral remote sensing imagery.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Support tensor machines for classification of hyperspectral remote sensing imagery

Reference 27

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This paper cites Tensors for data mining and data fusion: Models, applications, and scalable algorithms.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensors for data mining and data fusion: Models, applications, and scalable algorithms

Reference 28

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This paper cites Guarantees of Riemannian optimization for low rank matrix recovery.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Guarantees of Riemannian optimization for low rank matrix recovery

Reference 29

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On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Exact tensor completion using t-SVD

Reference 30

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This paper cites Linked component analysis from matrices to high-order tensors: Applica- tions to biomedical data.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Linked component analysis from matrices to high-order tensors: Applica- tions to biomedical data

Reference 31

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This paper cites Tensor decompositions and data fusion in epileptic electroencephalog- raphy and functional magnetic resonance imaging data.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensor decompositions and data fusion in epileptic electroencephalog- raphy and functional magnetic resonance imaging data

Reference 32

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This paper cites Low rank tensor recovery via iterative hard thresholding.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Low rank tensor recovery via iterative hard thresholding

Reference 33

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This paper cites Tensor decomposition for signal processing and machine learn- ing.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensor decomposition for signal processing and machine learn- ing

Reference 34

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This paper cites A practical randomized CP tensor decomposition.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery A practical randomized CP tensor decomposition

Reference 35

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This paper cites High-dimensional probability: An introduction with applications in data science.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery High-dimensional probability: An introduction with applications in data science

Reference 36

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This paper cites Sketched ridge regression: Opti- mization perspective, statistical perspective, and model averaging.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Sketched ridge regression: Opti- mization perspective, statistical perspective, and model averaging

Reference 37

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This paper cites Median-truncated nonconvex approach for phase retrieval with outliers.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Median-truncated nonconvex approach for phase retrieval with outliers

Reference 38

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This paper cites Almost Optimal Tensor Sketch.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Almost Optimal Tensor Sketch

Reference 39

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On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery TTHRESH: Tensor compres- sion for multidimensional visual data

Reference 40

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This paper cites Iterative hard thresholding for low-rank recovery from rank-one projections.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Iterative hard thresholding for low-rank recovery from rank-one projections

Reference 41

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This paper cites Tensorly: Tensor learning in python.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensorly: Tensor learning in python

Reference 42

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This paper cites Relative error tensor low rank approxima- tion.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Relative error tensor low rank approxima- tion

Reference 43

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This paper cites Oblivious sketching of high-degree polynomial kernels.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Oblivious sketching of high-degree polynomial kernels

Reference 44

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This paper cites Stochastic iterative hard thresholding for low-Tucker-rank tensor recovery.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Stochastic iterative hard thresholding for low-Tucker-rank tensor recovery

Reference 45

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This paper cites On recoverability of randomly compressed tensors with low CP rank.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery On recoverability of randomly compressed tensors with low CP rank

Reference 46

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This paper cites Concentration inequalities for random tensors.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Concentration inequalities for random tensors

Reference 47

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This paper cites Regularized Kaczmarz algorithms for tensor recovery.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Regularized Kaczmarz algorithms for tensor recovery

Reference 48

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This paper cites Lower memory oblivious (tensor) subspace embeddings with fewer ran- dom bits: modewise methods for least squares.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Lower memory oblivious (tensor) subspace embeddings with fewer ran- dom bits: modewise methods for least squares

Reference 49

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This paper cites Faster Johnson–Lindenstrauss transforms via Kronecker products.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Faster Johnson–Lindenstrauss transforms via Kronecker products

Reference 50

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Observation 703e4dd1-3df3-4b47-8332-d3be3a11d2b4 · outbound

This paper cites Tensor Random Projection for Low Memory Dimension Reduction.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensor Random Projection for Low Memory Dimension Reduction

Reference 51

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This paper cites Iterative hard thresholding with adaptive regu- larization: Sparser solutions without sacrificing runtime.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Iterative hard thresholding with adaptive regu- larization: Sparser solutions without sacrificing runtime

Reference 52

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This paper cites On block accelerations of quantile randomized Kaczmarz for corrupted systems of linear equations.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery On block accelerations of quantile randomized Kaczmarz for corrupted systems of linear equations

Reference 53

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Observation a2d852e8-bd84-414b-a85c-cec1e2c8c7cc · outbound

This paper cites Quantile-based iterative methods for corrupted systems of linear equa- tions.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Quantile-based iterative methods for corrupted systems of linear equa- tions

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:03:54.356578Z

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Observation 560930ae-142d-406f-be4b-5d5905f47145 · outbound

This paper cites Near-isometric properties of Kronecker-structured random tensor embeddings.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Near-isometric properties of Kronecker-structured random tensor embeddings

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:03:54.345920Z

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Observation 627981d0-3da5-4380-9af6-b5ed3341163b · outbound

This paper cites Optimal statistical estimation: sub-Gaussian properties, heavy- tailed data, and robust-ness.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Optimal statistical estimation: sub-Gaussian properties, heavy- tailed data, and robust-ness

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:03:54.334263Z

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Observation 44dd3b9b-40cb-4014-8fba-a915a29daa48 · outbound

This paper cites Iterative Singular Tube Hard Thresholding Algorithms for Tensor Recovery.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Iterative Singular Tube Hard Thresholding Algorithms for Tensor Recovery

Reference 57

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Observation 01761e7a-e50a-4509-8c64-3f970c9c6034 · outbound

This paper cites Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:03:54.256642Z

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Observation 2cb2cf87-2648-4c3e-9a7b-ad4714d3128f · outbound

This paper cites Modewise operators, the tensor restricted isometry property, and low-rank tensor recovery.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Modewise operators, the tensor restricted isometry property, and low-rank tensor recovery

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:03:54.323662Z

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Observation 4779ec35-4217-4f24-8233-31dbe9e493ac · outbound

This paper cites Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints

Reference 60

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Observation ccb375ec-0a3f-44a3-91ea-e2a8d579074d · outbound

This paper cites Low-rank tensor estimation via Riemannian Gauss-Newton: Statistical optimality and second-order convergence.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Low-rank tensor estimation via Riemannian Gauss-Newton: Statistical optimality and second-order convergence

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:03:54.314137Z

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Observation 56f66039-eb88-43de-9270-ae370f4a92a1 · outbound

This paper cites Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:03:54.304352Z

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Observation 37c84720-5940-4a3b-900c-d0ff718a522a · outbound

This paper cites Tensor Decompositions for Data Science.

On Trimming Tensor-structured Measurements and Efficient Low-rank Tensor Recovery Tensor Decompositions for Data Science

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T11:03:54.295247Z

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