Robust LDA Classification by Subsampling

In this paper we present a new method which enables a robust calculation of the LDA classification rule, thus making the recognition of objects under non-ideal conditions possible, i.e., in situations when objects are occluded or they appear on a varying background, or when their images are corrupted by outliers. The main idea behind the method is to translate the task of calculating the LDA classification rule into the problem of determining the coefficients of an augmented generative model (PCA). Specifically, we construct an augmented PCA basis which, on the one hand, contains information necessary for the classification (in the LDA sense), and, on the other hand, enables us to calculate the necessary coefficients by means of a subsampling approach resulting in a high breakdown point classification. The theoretical results are evaluated on the ORL face database showing that the proposed method significantly outperforms the standard LDA.