An Accelerated Rank-(L, L, 1, 1) Block Term Decomposition Of Multi-Subject Fmri Data Under Spatial Orthonormality Constraint
Li-Dan Kuang, Biao Wang, Qiu-Hua Lin, Haopeng Zhang, Jianming Zhang, Wenjun Li, Feng Li, Vince D. Calhoun
Abstract
The decomposition of multi-subject fMRI data using rank-(L,L,1,1) block term decomposition (BTD) can preserve higher-way data structure and is more robust to noise effects by decomposing shared spatial maps (SMs) into a product of two rank-L loading matrices. However, since the number of whole-brain voxels is very large and rank L is larger than 1, the rank-(L,L,1,1) BTD requires high computation and memory. Therefore, we propose an accelerated rank-(L,L,1,1) BTD algorithm based upon the method of alternating least squares (ALS). We speed up updates of loading matrices by reducing fMRI data into subspaces, and add an orthonormality constraint on shared SMs to improve the performance. Moreover, we evaluate the rank-L effect on the proposed method for actual task-related fMRI data. The proposed method shows better performance when L=35. Meanwhile, experimental comparison results verify that the proposed method largely reduced (17.36 times) computation time compared to ALS while also providing satisfying separation performance.
BibTeX
@inproceedings{icassp2022_anacceleratedran,
title = {An Accelerated Rank-(L, L, 1, 1) Block Term Decomposition Of Multi-Subject Fmri Data Under Spatial Orthonormality Constraint},
author = {Li-Dan Kuang and Biao Wang and Qiu-Hua Lin and Haopeng Zhang and Jianming Zhang and Wenjun Li and Feng Li and Vince D. Calhoun},
booktitle = {ICASSP 2022},
year = {2022}
}