Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms | Paper Citation Record

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This paper cites Structured data fusion,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Structured data fusion,

Reference 1

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This paper cites Under- standing data fusion within the framework of coupled matrix and tensor factorizations,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Under- standing data fusion within the framework of coupled matrix and tensor factorizations,

Reference 2

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This paper cites Multimodal data fusion: an overview of methods, challenges, and prospects,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Multimodal data fusion: an overview of methods, challenges, and prospects,

Reference 3

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This paper cites Structure-revealing data fusion,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Structure-revealing data fusion,

Reference 4

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This paper cites Chapter 10 - Coupled tensor decompositions for data fusion,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Chapter 10 - Coupled tensor decompositions for data fusion,

Reference 5

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This paper cites Turbo-SMT: Accelerating coupled sparse matrix-tensor factorizations by 200x - Suplementary material,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Turbo-SMT: Accelerating coupled sparse matrix-tensor factorizations by 200x - Suplementary material,

Reference 6

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This paper cites Link prediction in heterogeneous data via generalized coupled tensor factorization,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Link prediction in heterogeneous data via generalized coupled tensor factorization,

Reference 7

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This paper cites Hyperspectral super- resolution with coupled tucker approximation: Recoverability and SVD- based algorithms,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Hyperspectral super- resolution with coupled tucker approximation: Recoverability and SVD- based algorithms,

Reference 8

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This paper cites Hyperspec- tral super-resolution: A coupled tensor factorization approach,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Hyperspec- tral super-resolution: A coupled tensor factorization approach,

Reference 9

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This paper cites Coupled tensor decomposition for hyperspectral and multispectral image fusion with inter-image variability,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled tensor decomposition for hyperspectral and multispectral image fusion with inter-image variability,

Reference 10

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This paper cites Hyperspectral super-resolution accounting for spectral variability: Coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Hyperspectral super-resolution accounting for spectral variability: Coupled tensor LL1-based recovery and blind unmixing of the unknown super-resolution image,

Reference 11

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This paper cites Coupled canonical polyadic decomposition of multi-group fMRI data with spatial reference and orthonormality constraints,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled canonical polyadic decomposition of multi-group fMRI data with spatial reference and orthonormality constraints,

Reference 12

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This paper cites Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion,

Reference 13

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This paper cites Multidimensional harmonic retrieval via coupled canonical polyadic decomposition–Part II: Algorithm and multirate sampling,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Multidimensional harmonic retrieval via coupled canonical polyadic decomposition–Part II: Algorithm and multirate sampling,

Reference 15

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Observation 947bdb30-0a78-4e5b-b4b1-33c6ad7ab2a5 · outbound

This paper cites Multiple invariance ESPRIT for nonuniform linear arrays: A cou- pled canonical polyadic decomposition approach,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Multiple invariance ESPRIT for nonuniform linear arrays: A cou- pled canonical polyadic decomposition approach,

Reference 16

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Observation 32f5e9c4-482f-4aeb-a295-dfb491ed7b94 · outbound

This paper cites Coupled canonical polyadic decompositions and multiple shift invariance in array process- ing,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled canonical polyadic decompositions and multiple shift invariance in array process- ing,

Reference 17

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Observation 17a3a7fb-c273-4a0d-9f6c-e635e3af27b0 · outbound

This paper cites Coupled coarray tensor CPD for DOA estimation with coprime L-shaped array,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled coarray tensor CPD for DOA estimation with coprime L-shaped array,

Reference 18

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This paper cites Coupled and incomplete tensors in blind system identification,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled and incomplete tensors in blind system identification,

Reference 19

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Observation 08e7bcb2-91c8-4543-8c07-87e0d7530b61 · outbound

This paper cites Double cou- pled canonical polyadic decomposition for joint blind source separation,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Double cou- pled canonical polyadic decomposition for joint blind source separation,

Reference 20

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms PARAFAC-based blind estimation of pos- sibly underdetermined convolutive MIMO systems,

Reference 21

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This paper cites Data fusion in metabolomics using coupled matrix and tensor factorizations,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Data fusion in metabolomics using coupled matrix and tensor factorizations,

Reference 22

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Observation 2b8f82c2-9e3c-427f-846e-b127af942189 · outbound

This paper cites Tensor analysis and fusion of multimodal brain images,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Tensor analysis and fusion of multimodal brain images,

Reference 23

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis,

Reference 24

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Learning spatiotemporal brain dynamics in adolescents via multimodal MEG and fMRI data fusion using joint tensor/matrix decomposition,

Reference 25

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This paper cites ICA and IV A for data fusion: An overview and a new approach based on disjoint subspaces,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms ICA and IV A for data fusion: An overview and a new approach based on disjoint subspaces,

Reference 26

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled matrix-tensor factorizations– the case of partially shared factors,

Reference 27

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Common and distinct components in data fusion,

Reference 28

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Unraveling diagnostic biomarkers of schizophrenia through structure- revealing fusion of multi-modal neuroimaging data,

Reference 29

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Probabilistic linear discriminant analysis for inferences about identity,

Reference 30

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Deep hyperspectral and multispectral image fusion with inter-image variability,

Reference 31

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Mining labelled tensors by discovering both their common and discriminative subspaces,

Reference 32

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Pairfac: Event analytics through discriminant tensor factorization,

Reference 33

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Observation deb76b0b-0d56-4eb8-82f1-f133764375cc · outbound

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Multi-set low-rank factorizations with shared and unshared components,

Reference 34

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms A flexible optimization frame- work for regularized matrix-tensor factorizations with linear couplings,

Reference 35

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Observation cc7d685a-2481-417a-aa18-88b7f5f53dea · outbound

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Exploring multimodal data fusion through joint decompositions with flexible couplings,

Reference 36

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Observation e0d516fe-f459-4b75-ac7d-2559ad2431f9 · outbound

This paper cites Reproducibility in matrix and tensor decompositions: Focus on model match, interpretability, and uniqueness,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Reproducibility in matrix and tensor decompositions: Focus on model match, interpretability, and uniqueness,

Reference 37

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Observation 706f1e9e-2724-48c0-99e8-7dae6a374b0b · outbound

This paper cites Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank- (Lr,n,Lr,n,1) terms–part I: Uniqueness,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank- (Lr,n,Lr,n,1) terms–part I: Uniqueness,

Reference 38

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Observation 6be46f99-cd39-423a-8838-4d9a70b1078d · outbound

This paper cites Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank-(Lr,n,Lr,n,1) terms–part II: Algorithms,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank-(Lr,n,Lr,n,1) terms–part II: Algorithms,

Reference 39

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Observation dfbcc88a-abb7-4f44-8c42-89566b5ca5e0 · outbound

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Tensor completion from regular sub-Nyquist samples,

Reference 40

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Observation 80ed2e96-f907-4622-8cc9-e284571b463f · outbound

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Spectrum cartog- raphy via coupled block-term tensor decomposition,

Reference 41

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Observation f77fcc2d-7d85-4c7f-b8a7-9d5630c62854 · outbound

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Personalized PCA: Decoupling shared and unique features,

Reference 42

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Observation 67edfb37-d092-40a3-82ce-a2813af77cac · outbound

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Personalized dictionary learning for heterogeneous datasets,

Reference 43

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Observation 1faf3160-6209-4d80-8639-166e54f79aa7 · outbound

This paper cites Heterogeneous Matrix Factorization: When Features Differ by Datasets.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Heterogeneous Matrix Factorization: When Features Differ by Datasets

Reference 44

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Observation f4b9fe49-e6e1-44fe-8051-6a0879f3c110 · outbound

This paper cites Uni-mode and partial uniqueness conditions for CANDECOMP/PARAFAC of three-way ar- rays with linearly dependent loadings,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Uni-mode and partial uniqueness conditions for CANDECOMP/PARAFAC of three-way ar- rays with linearly dependent loadings,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.433086Z

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Observation 9ffe18bc-8b49-490b-9b66-5210d593f060 · outbound

This paper cites On the uniqueness of the canonical polyadic decomposition of third-order tensors–Part I: Basic results and uniqueness of one factor matrix,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms On the uniqueness of the canonical polyadic decomposition of third-order tensors–Part I: Basic results and uniqueness of one factor matrix,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.411911Z

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Observation 7b072caa-e51f-4b39-b4e3-07466b431816 · outbound

This paper cites Tensor decompositions and applications,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Tensor decompositions and applications,

Reference 47

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Observation 065e48e0-8d7a-4e68-a47c-b384682566cb · outbound

This paper cites Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.376016Z

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Observation 2d5af6f1-1446-48fa-b093-d367ab0f0990 · outbound

This paper cites On Kruskal’s uniqueness condi- tion for the Candecomp/Parafac decomposition,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms On Kruskal’s uniqueness condi- tion for the Candecomp/Parafac decomposition,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.357845Z

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Observation d25fcd89-6c3e-4cee-972a-e1b20e089bc5 · outbound

This paper cites On the uniqueness of the canonical polyadic decomposition of third-order tensors–Part II: Uniqueness of the overall decomposition,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms On the uniqueness of the canonical polyadic decomposition of third-order tensors–Part II: Uniqueness of the overall decomposition,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.340948Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 746f1e20-56d2-4754-8b88-789695bdfe6a · outbound

This paper cites On generic identifiability of 3-tensors of small rank,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms On generic identifiability of 3-tensors of small rank,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.322973Z

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Observation 54c99613-21a6-4b95-8e9e-e1261ea5f721 · outbound

This paper cites Canonical polyadic decomposition of third-order tensors: Reduction to generalized eigenvalue decomposition,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Canonical polyadic decomposition of third-order tensors: Reduction to generalized eigenvalue decomposition,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.304075Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 09035fbd-4e99-49e6-90eb-3964485fc624 · outbound

This paper cites Decompositions of a higher-order tensor in block terms–part II: Definitions and uniqueness,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Decompositions of a higher-order tensor in block terms–part II: Definitions and uniqueness,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.285483Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 99bdeeb8-e9b6-4b31-9c59-5c98efb097c9 · outbound

This paper cites CANDELINC: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms CANDELINC: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.267230Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91dd99b2-cdca-47a4-b592-9bb306d9a4dd · outbound

This paper cites Quantifying the interaction and contribution of multiple datasets in fusion: Application to the detection of schizophrenia,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Quantifying the interaction and contribution of multiple datasets in fusion: Application to the detection of schizophrenia,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.250826Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e3217e28-f38a-4852-9afc-5ec3b3c6bc6d · outbound

This paper cites Blind fMRI source unmixing via higher-order tensor decompositions,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Blind fMRI source unmixing via higher-order tensor decompositions,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.232444Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 62df9221-aa1a-4790-86c1-7efe465c4de4 · outbound

This paper cites Non-local tensor sparse coding for multi- image super-resolution in magnetic resonance imaging,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Non-local tensor sparse coding for multi- image super-resolution in magnetic resonance imaging,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.215212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6970e625-f333-49d4-bb51-61312cc79c3c · outbound

This paper cites Multi-frame super-resolution MRI using coupled low-rank tucker approximation,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Multi-frame super-resolution MRI using coupled low-rank tucker approximation,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T04:54:03.395254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c9ac85a-92e9-4e14-914b-fd4633b037d7 · outbound

This paper cites AFFIRM: affinity fusion-based framework for iteratively random motion correction of multi-slice fetal brain MRI,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms AFFIRM: affinity fusion-based framework for iteratively random motion correction of multi-slice fetal brain MRI,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.196181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 29599a84-700e-4387-bf61-7bfa666c999d · outbound

This paper cites Spectral variability in hyperspectral data unmixing: A comprehensive review,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Spectral variability in hyperspectral data unmixing: A comprehensive review,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.175968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3f40e158-851d-49a7-804a-63c170a6fba3 · outbound

This paper cites Tensor- based fusion of EEG and FMRI to understand neurological changes in schizophrenia,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Tensor- based fusion of EEG and FMRI to understand neurological changes in schizophrenia,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.158695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 89734c19-bfe7-4181-986e-3440a0602bc8 · outbound

This paper cites Longitudinal metabolomics data analysis informed by mechanistic models,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Longitudinal metabolomics data analysis informed by mechanistic models,

Reference 62

Resolution: verified exact; doi, observed 2026-08-12T04:54:03.586486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aad1b501-c089-417a-a64e-c7107a007ab0 · outbound

This paper cites an unresolved cited work.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:54:04.141780Z

Source-reported events for the cited work

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Observation a27bb664-78e4-4642-a68f-81d1245418cb · outbound

This paper cites Nonlinear multiview analysis: Identifiability and neural network-assisted implementation,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Nonlinear multiview analysis: Identifiability and neural network-assisted implementation,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.123897Z

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Observation 0283f678-04c8-41d0-8260-f85d5ff258f9 · outbound

This paper cites The zero set of a polynomial,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms The zero set of a polynomial,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.100147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1d07b6e4-bdb4-4215-86c3-271e2429903f · outbound

This paper cites Comput- ing large-scale matrix and tensor decomposition with structured factors: A unified nonconvex optimization perspective,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Comput- ing large-scale matrix and tensor decomposition with structured factors: A unified nonconvex optimization perspective,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.079583Z

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Observation 711f5610-67c2-4443-8265-ba073e80158a · outbound

This paper cites Enhanced line search: A novel method to accelerate PARAFAC,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Enhanced line search: A novel method to accelerate PARAFAC,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.060536Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 86341544-2dc6-49f9-a35d-c5d1ff08d007 · outbound

This paper cites On minimum-cost assignments in unbalanced bipartite graphs,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms On minimum-cost assignments in unbalanced bipartite graphs,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.042056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eb6dd85c-a7c0-46d2-afa4-5aefaa2c1d06 · outbound

This paper cites Generalized canonical polyadic tensor decomposition,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Generalized canonical polyadic tensor decomposition,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:04.020972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 54c16078-6ad7-4c94-9897-f9e07fd4f836 · outbound

This paper cites Stochastic mirror descent for low-rank tensor decomposition under non-euclidean losses,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Stochastic mirror descent for low-rank tensor decomposition under non-euclidean losses,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.996227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fe299dc3-327a-4307-a93c-73f728e20aa4 · outbound

This paper cites Nonnegative block-term decomposition with the β-divergence: Joint data fusion and blind spectral unmixing,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Nonnegative block-term decomposition with the β-divergence: Joint data fusion and blind spectral unmixing,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.978144Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d33d7192-5bc6-4e3a-bdd7-e095ef027fdc · outbound

This paper cites Tensor decompositions, alternating least squares and other tales,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Tensor decompositions, alternating least squares and other tales,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.959857Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a30b75a5-5a6b-458e-8a71-e34770c780a0 · outbound

This paper cites Vervliet, O.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Vervliet, O

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.941452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c4c77dde-d68d-4659-a78d-52a5ff64f2ba · outbound

This paper cites Cloud detection over the Arctic region using airborne imaging spectrometer data during the daytime,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Cloud detection over the Arctic region using airborne imaging spectrometer data during the daytime,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.923867Z

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Observation 53ef2145-4060-4281-bfe0-84b96a340bfe · outbound

This paper cites Super-resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Super-resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.903826Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44f9afb7-311e-44d9-b56a-dafcd1bac431 · outbound

This paper cites Disjoint subspaces for common and distinct component analysis: Ap- plication to the fusion of multi-task fMRI data,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Disjoint subspaces for common and distinct component analysis: Ap- plication to the fusion of multi-task fMRI data,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.882373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c64b65fc-16e3-42ab-8552-23b97a881cce · outbound

This paper cites Estimation of common subspace order across multiple datasets: Application to multi-subject fMRI data,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Estimation of common subspace order across multiple datasets: Application to multi-subject fMRI data,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.856426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fb4074d-eea8-4bff-a720-477b7f76358b · outbound

This paper cites Determining the dimension and structure of the subspace correlated across multiple data sets,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Determining the dimension and structure of the subspace correlated across multiple data sets,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.834421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e5e93b64-d09e-4459-8549-5ba0a40da991 · outbound

This paper cites Solution of the matrix equation AX+XB=C,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Solution of the matrix equation AX+XB=C,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.815143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e0a5ef9-193a-4272-8c4d-9b24b6bbca28 · outbound

This paper cites Solution of the Sylvester matrix equation axb t+ cxd t= e,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Solution of the Sylvester matrix equation axb t+ cxd t= e,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.793827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6c5d096e-8033-4ada-bcae-4df0f57e67f2 · outbound

This paper cites A Hessenberg-Schur method for the problem ax+xb=c,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms A Hessenberg-Schur method for the problem ax+xb=c,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.775857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea3b20b7-6106-453b-9dde-63d6ea9c02fd · outbound

This paper cites Computational methods for linear matrix equations,.

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms Computational methods for linear matrix equations,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:54:03.755883Z

Source-reported events for the cited work

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