Linear discriminant analysis (LDA) as a well-known supervised dimensionality reduction method has been widely applied in many fields. However, the lack of sparsity in the LDA solution makes interpretation of the results challenging. In this paper, we propose a new model for sparse uncorrelated LDA (ULDA). Our model is based on the characterization of all solutions of the generalized ULDA. We incorporate sparsity into the ULDA transformation by seeking the solution with minimum    $\ell _{1}$   -norm from all minimum dimension solutions of the generalized ULDA. The problem is then formulated as an    $\ell _{1}$   -minimization problem with orthogonality constraint. To solve this problem, we devise two algorithms: 1) by applying the linearized alternating direction method of multipliers and 2) by applying the accelerated linearized Bregman method. Simulation studies and high-dimensional real data examples demonstrate that our algorithms not only compute extremely sparse solutions but also perform well in classification.
['Xiaowei Zhang', 'Delin Chu', 'Roger C. E. Tan']
Sparse Uncorrelated Linear Discriminant Analysis for Undersampled Problems