Unsupervised Sparse Multi‐Task Learning With Application to Alzheimer's Disease

for sparsity control) provide a principled trade-off between biological fidelity and model complexity. Computationally, we develop a DC-ADMM algorithm that transforms the optimization into tractable subproblems, establishing finite-step convergence to KKT points. Comprehensive simulation studies and real-data analysis of AD neuroimaging data demonstrate SMART's superior accuracy in connectivity biomarker identification, enhanced stability in feature selection, and improved interpretability for AD cohort studies. An accompanying R package, SMART, is available on GitHub ( https://github.com/LDstat/SMART).