The sparse group lasso for high-dimensional integrative linear discriminant analysis with application to alzheimer's disease prediction

Abstract

In the big data era, high-dimensional data from multi-type data sources are ubiquitous and pose great challenge for statistical analysis. In this paper, we focus on two-class classification problem for this kind of data. Motivated by the least squares formulation of linear discriminant analysis, we propose an integrative discrimination analysis method by virtue of sparse group Lasso penalty. Thorough numerical studies are conducted and the results show that the proposed method compares favourably with other popular sparse discriminant proposals in terms of both variable selection accuracy and classification error. A real-data example is also given to illustrate its advantages.