Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis
Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spatial and temporal patterns exhibit rich variability across subjects. Existing matrix and tensor decompositions provide interpretable factorizations, but rely on fixed multilinear structures or coupling schemes that may limit their flexibility in capturing complex variability. In this work, we introduce a spatiotemporal variational tensor decomposition (ST-VTD) framework that combines a tensor factorization generative model with structured priors to jointly represent spatial maps and temporal dynamics. Spatial factors are regularized to promote...
| Authors | Laura M. Montaldo, Ricardo A. Borsoi, Sebastian Miron et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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