194 citations · 312 across the 10 of their papers we have counts for
11 papers
Sample Dependence-Aware Blind Source Separation for Linear Causal Discovery
Cihan Eralp Kumbasar, Emin Erdem Kumbasar, Tulay Adali
Causal discovery seeks to determine directional relationships among variables in complex systems, yet observational data alone generally do not identify causal direction. Complete…
Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis
Laura M. Montaldo, Ricardo A. Borsoi, Sebastian Miron +1
Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spatial and temporal patterns exhib…
Isolating Nonlinear Independent Sources in fMRI with -TCVAE Models
Qiang Li, Shujian Yu, Jesus Malo +3
Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent component analysis, widely use…
Deep Deterministic Nonlinear ICA via Total Correlation Minimization with Matrix-Based Entropy Functional
Qiang Li, Shujian Yu, Liang Ma +4
Blind source separation, particularly through independent component analysis (ICA), is widely utilized across various signal processing domains for disentangling underlying compone…
Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms
Ricardo Augusto Borsoi, Konstantin Usevich, David Brie +1
Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address imp…
An Effective Iterative Solution for Independent Vector Analysis with Convergence Guarantees
Clément Cosserat, Ben Gabrielson, Emilie Chouzenoux +2
Independent vector analysis (IVA) is an attractive solution to address the problem of joint blind source separation (JBSS), that is, the simultaneous extraction of latent sources f…
Copula-Linked Parallel ICA: A Method for Coupling Structural and Functional MRI brain Networks
Oktay Agcaoglu, Rogers F. Silva, Deniz Alacam +3
Different brain imaging modalities offer unique insights into brain function and structure. Combining them enhances our understanding of neural mechanisms. Prior multimodal studies…
Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis
Trung Vu, Francisco Laport, Hanlu Yang +2
Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis…
An AO-ADMM approach to constraining PARAFAC2 on all modes
Marie Roald, Carla Schenker, Vince D. Calhoun +4
Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example,…
Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data
Guoxu Zhou, Qibin Zhao, Yu Zhang +3
With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data th…
Independent Vector Analysis: Identification Conditions and Performance Bounds
Matthew Anderson, Geng-Shen Fu, Ronald Phlypo +1
Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been the subject of significant research…