Elastic Net based sparse feature learning and classification for Alzheimer's disease identification.

Abstract

Magnetic resonance imaging (MRI) has been successfully applied to investigate neuron pathological changes. Since the high dimension of observation data, sparse feature learning plays an important role in overcoming the challenge of high variable dimension and low sample size problem among the disease identification. In this paper, sparse Elastic Net (EN) was used to extract low dimension features and to identify the Alzheimer's disease (AD). Compare with principal component analysis (PCA) method, the EN method can solve the problems of less samples and high correlations between variables. For those variables sharing the same biological phenomenon, it selected whole groups into the model automatically once one variable among them was selected. Unlike other subspace learning methods, the proposed method used less man-made feature setting. The problems of dimension reduction and classification were conducted into a similar formulation. Experimental results illustrated the effectiveness of the proposed method.