Early stages of neurodegenerative diseases draw increasing recognition as obscure symptoms may appear before classical clinical diagnosis. For this reason, we propose a novel multi-task low-rank feature learning method, which takes advantages of the sparsity and low-rankness of neuroimaging data for Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) multi-classification. First, the low-rank learning is proposed to unveil the underlying relationships between input data and output targets by preserving the most class-discriminative features. Multi-task learning is simultaneously performed to capture intrinsic feature relatedness. A sparse linear regression framework is designed to find the low-dimensional structure of high dimensional data. Experimental results on the Parkinson’s progression markers initiative (PPMI) and Alzheimer’s disease neuroimaging initiative (ADNI) datasets show that our proposed model not only enhances the performances of multi-classification tasks, but also outperforms the conventional algorithms.