Independent vector analysis (IVA) can be viewed as an extension of independent
component analysis (ICA) to multiple datasets. It exploits the statistical dependency between …

Joint blind source separation (JBSS) involves the factorization of multiple matrices, ie
“datasets”, into “sources” that are statistically dependent across datasets and independent …

The demand for adaptable approaches to analyze extensive fMRI data is growing, focusing
on capturing population patterns while preserving individual uniqueness. Independent …

In this paper, we introduce the constrained independent vector analysis integrated with the
bounded multivariate generalized Gaussian mixture model (cIVABMGGMM) to tackle the …

Joint blind source separation (JBSS) is a powerful framework for extracting latent sources
from multiple datasets while keeping their coherence across multiple linked datasets …

Multi-subject fMRI data is instrumental in understanding the brain function and studying
different brain disorders. It is desirable to analyze fMRI datasets jointly to leverage the cross …

Matrix and tensor factorizations are methods that decompose datasets into factors:
unobserved, estimated variables useful for summarizing a dataset's latent structure. The …