发表机构
CRAN, Université de Lorraine, CNRS, Vandoeuvre-lès-Nancy, France（CRAN、洛林大学、CNRS、法国凡多伊-勒-纳恩西）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多主体时空数据建模难题，提出时空变分张量分解框架，结合张量分解模型与结构化先验，利用LL1和LSTM分别处理空间和时间因子，经展开优化算法迭代推理，热启动策略提升性能，实验显示其优于基准方法。
AI 中文摘要
在多主体时空数据中对共享和特定主体结构进行建模仍然具有挑战性，尤其是在神经成像领域。现有分解方法存在局限性。本文引入时空变分张量分解（ST-VTD）框架，结合张量分解生成模型与结构化先验来联合表示空间图和时间动态。空间因子通过类似LL1分解进行正则化以促进低秩结构，时间因子使用基于长短期记忆（LSTM）的先验建模。通过展开优化算法迭代进行后验推理，采用基于组独立成分分析的热启动策略。实验表明该方法在潜在因子恢复方面显著优于经典和概率分解基准。
英文摘要
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 a low-rank structure inspired by the LL1 decomposition, while temporal factors are modeled using a learned Long short-term memory (LSTM)-based prior, enabling flexible and adaptive dynamics. Posterior inference is performed using an amortized variational formulation by unrolling iterations of an optimization algorithm, leading to an interpretable and parameter-efficient architecture. The proposed inference framework employs a warm-start strategy based on group independent component analysis, which we found to improve optimization performance. Experiments on a realistic synthetic functional MRI (fMRI) dataset demonstrate that the proposed approach significantly improves latent factor recovery compared with representative classical and probabilistic decomposition benchmarks.
Comments6 pages, 2 figures, conference
