Ensemble prediction of longitudinal scores of Alzheimer's disease based on ℓ<inf>2,1</inf>-norm regularized correntropy with spatial-temporal constraint

Abstract

This paper presents a novel longitudinal framework for clinical score prediction in Alzheimer's disease (AD) diagnosis. In contrast to the previous approaches that use the data collected at a single time point only for the clinical score prediction, we propose to exploit the imaging data of multiple time points. Furthermore, a spatial-temporal group sparse method is proposed for robust feature selection through imposing a fused smoothness term and a locality-preserving-projection based term as well as integrating correntropy into the framework, which is able to promote the prediction consistency and reduce the adverse effect of noises and outliers. Ensemble learning of support vector regression (SVR) is exploited to predict the AD scores more accurately with the selected features. The proposed approach is extensively evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset. The experiments demonstrate that our proposed approach achieves promising regression accuracy.