Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

ABSTRACT: Neuroimaging data have been widely used to derive possible biomarkers for Alzheimer's Disease (AD) diagnosis. As only certain brain regions are related to AD progression, many feature selection methods have been proposed to identify informative features (i.e., brain regions) to build an accurate prediction model. These methods mostly only focus on the feature-target relationship to select features which are discriminative to the targets (e.g., diagnosis labels). However, since the brain regions are anatomically and functionally connected, there could be useful intrinsic relationships among features. In this paper, by utilizing both the feature-target and feature-feature relationships, we propose a novel sparse regression model to select informative features which are discrimi

SUBMITTER: Zhu X 

PROVIDER: S-EPMC5612439 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature