A subset method for improving Linear Discriminant Analysis

Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it suffers from class separation problem for C-class when the reduced dimensionality is less than C−1. To cope with this problem, we propose a subset improving method in this paper. In the method, the subspaces are found for each subset rather than that for the entire data set. To partition the entire data set into subsets, a cost matrix is first estimated from the training set with the pre-learned classifier, then the graph cut method is adopted to minimize the cost between each subset. We use LDA to find subspaces for each subset. Experimental results based on different applications demonstrate both the generality and effectiveness of the proposed method. •We use the subset method to greatly improve the performance of Linear Discriminant Analysis in low-dimensional representations.•We propose a new classifier-specific based partition method.•Graph cut is adopted to solve the partition method.•The method could also be used to improve other linear discriminant algorithms.