Structure-revealing data fusion

Acar, Evrim; Papalexakis, Evangelos E.; Gürdeniz, Gözde; Rasmussen, Morten Arendt; Lawaetz, Anders J.; Nilsson, Mathias; Bro, Rasmus

doi:10.1186/1471-2105-15-239

Cited by 101 publications

(120 citation statements)

References 61 publications

Supporting

Mentioning

120

Contrasting

Order By: Relevance

“…The CP model is unique under mild conditions [19] and the ACMTF inherits uniqueness properties from CP [20]. In the presence of both shared/unshared components, 1-norm penalties on the weights help to obtain unique solutions [15].…”

Section: A Coupled Matrix/tensor Factorizationsmentioning

confidence: 99%

“…In order to fit the ACMTF model, we use ACMTF-OPT [15] from the CMTF Toolbox and the nonlinear conjugate gradient Parts of sr corresponding to the time samples for each electrode in EEG and voxels in fMRI and sMRI are plotted. In EEG plots, green dashed plots show signals from individual electrodes averaged across all subjects.…”

Section: B Experimental Settingmentioning

confidence: 99%

“…The CMTF model used in [3] assumes that all extracted factors are shared by EEG and fMRI data. Rather than such a CMTF model, which may fail to provide a unique solution in the presence of both shared/unshared components [15], we use a structure-revealing CMTF model, known as the advanced CMTF (ACMTF) model [15], revealing the weights of components in each modality to identify shared/unshared factors and understand the contribution from each modality. Previously, we have shown the promise of the ACMTF model in a joint analysis of fMRI and multi-channel EEG data in terms of capturing neural patterns that can differentiate patients with schizophrenia from healthy controls [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ACMTF for fusion of multi-modal neuroimaging data and identification of biomarkers

Acar

Levin-Schwartz

Calhoun

2017

2017 25th European Signal Processing Conference (EUSIPCO)

Self Cite

View full text Add to dashboard Cite

Abstract-Joint analysis of neuroimaging data from multiple modalities has the potential to improve our understanding of brain function since each modality provides complementary information. In this paper, we address the problem of jointly analyzing functional magnetic resonance imaging (fMRI), structural MRI (sMRI) and electroencephalography (EEG) data collected during an auditory oddball (AOD) task with the goal of capturing neural patterns that differ between patients with schizophrenia and healthy controls. Traditionally, fusion methods such as joint independent component analysis (jICA) have been used to jointly analyze such multi-modal neuroimaging data. However, previous jICA analyses typically analyze the EEG signal from a single electrode or concatenate signals from multiple electrodes, thus ignoring the potential multilinear structure of the EEG data, and models the data using a common mixing matrix for both modalities. In this paper, we arrange the multi-channel EEG signals as a third-order tensor with modes: subjects, time samples and electrodes, and jointly analyze the tensor with the fMRI and sMRI data, both in the form of subjects by voxels matrices, using a structure-revealing coupled matrix and tensor factorization (CMTF) model. Through this modeling approach, we (i) exploit the multilinear structure of multi-channel EEG data and (ii) capture weights for components indicative of the level of contribution from each modality. We compare the results of the structurerevealing CMTF model with those of jICA and demonstrate that, while both models capture significant distinguishing patterns between patients and controls, the structure-revealing CMTF model provides more robust activation.

show abstract

Section: A Coupled Matrix/tensor Factorizationsmentioning

confidence: 99%

Section: B Experimental Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ACMTF for fusion of multi-modal neuroimaging data and identification of biomarkers

Acar

Levin-Schwartz

Calhoun

2017

2017 25th European Signal Processing Conference (EUSIPCO)

Self Cite

View full text Add to dashboard Cite

show abstract

“…From the patterns of these 0's, the common and distinct components are defined, and thus, this method also obeys Equation . This type of approach—albeit in fusing 3‐ and 2‐way data—has been used in metabolomics . Structure‐revealing data fusion is within and between block scale dependent.…”

Section: How Established Methods Relate To the Definitionsmentioning

confidence: 99%

“…This type of approach-albeit in fusing 3-and 2-way data-has been used in metabolomics. 7,16 Structure-revealing data fusion is within and between block scale dependent.…”

Section: Structure-revealing Data Fusionmentioning

confidence: 99%

Common and distinct components in data fusion

Smilde

Måge

Næs

et al. 2017

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

In many areas of science multiple sets of data are collected pertaining to the same system. Examples are food products which are characterized by different sets of variables, bio-processes which are on-line sampled with different instruments, or biological systems of which different genomics measurements are obtained. Data fusion is concerned with analyzing such sets of data simultaneously to arrive at a global view of the system under study. One of the upcoming areas of data fusion is exploring whether the data sets have something in common or not. This gives insight into common and distinct variation in each data set, thereby facilitating understanding the relationships between the data sets. Unfortunately, research on methods to distinguish common and distinct components is fragmented, both in terminology as well as in methods: there is no common ground which hampers comparing methods and understanding their relative merits. This paper provides a unifying framework for this subfield of data fusion by using rigorous arguments from linear algebra. The most frequently used methods for distinguishing common and distinct components are explained in this framework and some practical examples are given of these methods in the areas of (medical) biology and food science.

show abstract

Tensor Decompositions: Principles and Application to Food Sciences

Cohen,

Bro,

Comon

2023

Source Separation in Physical‐Chemical Sensing

View full text Add to dashboard Cite

Tensors of order d may be seen as arrays of entries indexed by d indices. They naturally appear as data arrays in applications such as chemistry, food science, forensics, environmental analysis and many other fields. Extracting and visualizing the underlying features from tensors is an important source separation problem. This chapter first describes an important class of data mining methods for tensors, namely low-rank tensor approximations (CPD, Tucker3) in the case of order d = 3. In such a case, striking differences already exist compared to low-rank approximations of matrices, which are tensors of order d = 2. Constrained decompositions and coupled decompositions, which are important variants of tensor decompositions, are also discussed in details, along with practical learning algorithms. Finally, tensor decompositions are illustrated as a tool for source separation in food sciences. In particular fluorescence spectroscopy, electrophoresis in gel, or chromatography especially coupled with mass spectrometry, are techniques where tensor decompositions are known to be useful. Some of the many other source separation problems that may be tackled with tensor decompositions are briefly discussed in the concluding remarks.

show abstract

Structure-revealing data fusion

Cited by 101 publications

References 61 publications

ACMTF for fusion of multi-modal neuroimaging data and identification of biomarkers

ACMTF for fusion of multi-modal neuroimaging data and identification of biomarkers

Common and distinct components in data fusion

Tensor Decompositions: Principles and Application to Food Sciences

Contact Info

Product

Resources

About