Joint feature and task aware multi-task feature learning for Alzheimer’s disease diagnosis

Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years. There have been efforts to identify the risk of developing AD in its earliest time. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing $\ell_{2,1}$-norm have been widely studied to select a discriminative feature subset from MRI features. However, they ignore the complex relationships among imaging markers and among cognitive outcomes. Constructing the relationships with simple Pearson correlation coefficient may degrade model generalizability. To better capture the complicated but more flexible relationship between the cognitive scores and the neuroimaging measures, we propose a two-stage framework to jointly learn the structure within the feature correlation as well as within the task correlation. Moreover, we propose a dual graph regularization to encode the learned correlation structure. It is able to guide the training procedure of MTFL by incorporating both the inherent correlations. Extensive results on benchmark datasets show that for the proposed FTSMTFL model trained with the dual graph regularization, the proposed joint training framework outperforms existing methods and achieves state-of-the-art cognitive prediction performance of AD.