LogPar: Logistic PARAFAC2 factorization for temporal binary data with missing values
Binary data with one-class missing values are ubiquitous in real-world applications. They
can be represented by irregular tensors with varying sizes in one dimension, where value …
can be represented by irregular tensors with varying sizes in one dimension, where value …
An AO-ADMM approach to constraining PARAFAC2 on all modes
Analyzing multiway measurements with variations across one mode of the dataset is a
challenge in various fields including data mining, neuroscience, and chemometrics. For …
challenge in various fields including data mining, neuroscience, and chemometrics. For …
Tracing evolving networks using tensor factorizations vs. ica-based approaches
Analysis of time-evolving data is crucial to understand the functioning of dynamic systems
such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) …
such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) …
Exploring dynamic metabolomics data with multiway data analysis: a simulation study
Background Analysis of dynamic metabolomics data holds the promise to improve our
understanding of underlying mechanisms in metabolism. For example, it may detect …
understanding of underlying mechanisms in metabolism. For example, it may detect …
Dynamic community detection for brain functional networks during music listening with block component analysis
The human brain can be described as a complex network of functional connections between
distinct regions, referred to as the brain functional network. Recent studies show that the …
distinct regions, referred to as the brain functional network. Recent studies show that the …
Analyzing postprandial metabolomics data using multiway models: A simulation study
Background Analysis of time-resolved postprandial metabolomics data can improve the
understanding of metabolic mechanisms, potentially revealing biomarkers for early …
understanding of metabolic mechanisms, potentially revealing biomarkers for early …
Multi-task fMRI data fusion using IVA and PARAFAC2
Data fusion—the joint analysis of multiple datasets—through coupled factorizations has the
promise to enable enhanced knowledge discovery, and hence is an active area. Various …
promise to enable enhanced knowledge discovery, and hence is an active area. Various …
PARAFAC2 AO-ADMM: Constraints in all modes
The PARAFAC2 model provides a flexible alternative to the popular CANDECOMP/
PARAFAC (CP) model for tensor decompositions. Unlike CP, PARAFAC2 allows factor …
PARAFAC (CP) model for tensor decompositions. Unlike CP, PARAFAC2 allows factor …
Exploring oscillatory dysconnectivity networks in major depression during resting state using coupled tensor decomposition
W Liu, X Wang, T Hämäläinen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Dysconnectivity of large-scale brain networks has been linked to major depression disorder
(MDD) during resting state. Recent researches show that the temporal evolution of brain …
(MDD) during resting state. Recent researches show that the temporal evolution of brain …
PARAFAC2-based coupled matrix and tensor factorizations
C Schenker, X Wang, E Acar - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Coupled matrix and tensor factorizations (CMTF) have emerged as an effective data fusion
tool to jointly analyze data sets in the form of matrices and higher-order tensors. The …
tool to jointly analyze data sets in the form of matrices and higher-order tensors. The …