2020/11/11 by Katherine Tsai, Tsai, Katherine, Mladen Kolar +3 · 1 citation
Computer Science · Engineering · Mathematics · Neuroscience · #Applications (stat.AP) #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #cs.LG #stat.AP #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.2011.05601
openalex publication_date 2020/11/11 · arxiv created 2021/07/18 · arxiv updated 2021/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a flexible yet interpretable model for high-dimensional data with time-varying second order statistics, motivated and applied to functional neuroimaging data. Motivated by the neuroscience literature, we factorize the covariances into sparse spatial and smooth temporal components. While this factorization results in both parsimony and domain interpretability, the resulting estimation problem is nonconvex. To this end, we design a two-stage optimization scheme with a carefully tailored spectral initialization, combined with iteratively refined alternating projected gradient descent. We prove a linear convergence rate up to a nontrivial statistical error for the proposed descent scheme and establish sample complexity guarantees for the estimator. We further quantify the statistical error for the multivariate Gaussian case. Empirical results using simulated and real brain imaging data illustrate that our approach outperforms existing baselines.