Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer's Disease

Abstract

Alzheimer's disease (AD) has been not only the substantial financial burden to the health care system but also the emotional burden to patients and their families. Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Recently, the multitask learning (MTL) methods with sparsity-inducing norm (e.g., l2,1-norm) have been widely studied to select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, these previous works formulate the prediction tasks as a linear regression problem. The major limitation is that they assumed a linear relationship between the MRI features and the cognitive outcomes. Some multikernel-based MTL methods have been proposed and shown better generalization ability due to the nonlinear advantage. We quantify the power of existing linear and nonlinear MTL methods by evaluating their performance on cognitive score prediction of Alzheimer's disease. Moreover, we extend the traditional l2,1-norm to a more general lql1-norm (q≥1). Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the nonlinear l2,1lq-MKMTL method not only achieved better prediction performance than the state-of-The-Art competitive methods but also effectively fused the multimodality data.