Locally linear embedding (LLE) for MRI based Alzheimer's disease classification.

ABSTRACT: Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer's disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of local linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifi

SUBMITTER: Liu X 

PROVIDER: S-EPMC3815961 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature