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.

Keywords:

• Big data
• linear discriminant analysis
• integrative analysis
• spare group lasso

Acknowledgments

We would like to thank the editor and reviewers for their useful comments and suggestions, which have led to a significant improvement of this study. The results published here are in whole or in part based on data obtained from the AMP-AD Knowledge Portal (doi:10.7303/syn2580853). Study data were provided by the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago. Data collection was supported by NIA under Grant [number P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by grants from the National Key R&D Program of China [grant number 2018YFA0703900], the National Natural Science Foundation of China [grant numbers 11801316, 81803336, and 11871309], the Natural Science Foundation of Shandong Province [grant numbers ZR2018BH033 and ZR2019QA002].