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Isabell Lehmann

2 accepted papers

2023

Coupled CP Tensor Decomposition with Shared and Distinct Components for Multi-Task Fmri Data Fusion

ICASSP 2023accepted

Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data. Coupled matrix and tensor factorization approaches have been us…

Cited by 10SourceScholar
2022

Multi-Task fMRI Data Fusion Using IVA and PARAFAC2

ICASSP 2022accepted

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 formulations of coupled matrix factorizations have been proposed, each with its own modeling assumptions. In this paper, we…

Cited by 0SourceScholar