Exploiting task relationships for Alzheimer's disease cognitive score prediction via multi-task learning

Alzheimer's disease (AD) is highly prevalent and a significant cause of dementia and death in elderly individuals. Motivated by breakthroughs of multi-task learning (MTL), efforts have been made to extend MTL to improve the Alzheimer's disease cognitive score prediction by exploiting structure correlation. Though important and well-studied, three key aspects are yet to be fully handled in an unified framework: (i) appropriately modeling the inherent task relationship; (ii) fully exploiting the task relatedness by considering the underlying feature structure. (iii) automatically determining the weight of each task. To this end, we present the Bi-Graph guided self-Paced Multi-Task Feature Learning (BGP-MTFL) framework for exploring the relationship among multiple tasks to improve overall learning performance of cognitive score prediction. The framework consists of the two correlation regularization for features and tasks, l(2,1) regularization and self-paced learning scheme. Moreover, we design an efficient optimization method to solve the non -smooth objective function of our approach based on the Alternating Direction Method of Multipliers (ADMM) combined with accelerated proximal gradient (APG). The proposed model is comprehensively evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) datasets. Overall, the proposed algorithm achieves an nMSE (normalized Mean Squared Error) of 3.923 and an wR (weighted R-value) of 0.416 for predicting eighteen cognitive scores, respectively. The empirical study demonstrates that the proposed BGP-MTFL model outperforms the state-of-the-art AD prediction approaches and enables identifying more stable biomarkers.