Robust and Efficient Linear Discriminant Analysis With L2,1-Norm for Feature Selection

Feature selection and feature transformation are the two main approaches to reduce dimensionality, and they are often presented separately. In this study, a novel robust and efficient feature selection method, called FS-VLDA-L
_2,1
(feature selection based on variant of linear discriminant analysis and L
_2,1
-norm), is proposed by combining a new variant of linear discriminant analysis and L
_2,1
sparsity regularization. Here, feature transformation and feature selection are integrated into a unified optimization objective. To obtain significant discriminative power between classes, all the data in the same class are expected to be regressed to a single vector, and the important task is to explore a transformation matrix such that the squared regression error is minimized. Therefore, we derive a new discriminant analysis from a novel view of least squares regression. In addition, we impose row sparsity on the transformation matrix through L
_2,1
-norm regularized term to achieve feature selection. Consequently, the most discriminative features are selected, simultaneously eliminating the redundant ones. To address the L
_2,1
-norm based optimization problem, we design a new efficient iterative re-weighted algorithm and prove its convergence. Extensive experimental results on four well-known datasets demonstrate the performance of our feature selection method.