Feature-aware Multi-task feature learning for Predicting Cognitive Outcomes in Alzheimer's disease

Abstract

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Multi-task based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the inter-correlation among different brain regions. However, the multi-task based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multi-task learning approach via a joint sparsity-inducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.