Feature Extraction Using Fractional-Order Embedding Direct Linear Discriminant Analysis

Abstract

In this paper, a novel LDA-based dimensionality reduction method called fractional-order embedding direct LDA (FEDLDA) is proposed. More specifically, we redefine the fractional-order between-class and within-class scatter matrices which can significantly reduce the deviation of sample covariance matrices caused by the noise disturbance and limited number of training samples; then the novel feature extraction criterion based on the direct LDA (DLDA) and the idea of fractional-order embedding is applied. Experiments on AT&T, Yale and AR face image databases are performed to test and evaluate the effectiveness of the proposed algorithms. Extensive experimental results show that FEDLDA outperforms DLDA and other closely related methods in terms of classification accuracy and efficiency.