A novel supervised feature extraction algorithm: enhanced within-class linear discriminant analysis

Abstract

Linear discriminant analysis (LDA) is one of the most popular supervised feature extraction techniques used in machine learning and pattern classification. However, LDA only captures global structure information of the data and ignores the structure information of local data points. In this paper, a novel supervised feature extraction algorithm called enhanced within-class linear discriminant analysis (EWLDA) is proposed. More specifically, we define a local within-class scatter matrix to model the local structure information provided by local data samples. In order to balance the tradeoff between global and local structure information, a tuning parameter is also introduced. Experimental results on two image databases demonstrate the effectiveness of our algorithm.