Supervised locality pursuit embedding for pattern classification

In pattern recognition research, dimensionality reduction techniques are widely used since it may be difficult to recognize multidimensional data especially if the number of samples in a data set is not large comparing with the dimensionality of data space. Locality pursuit embedding (LPE) is a recently proposed method for unsupervised linear dimensionality reduction. LPE seeks to preserve the local structure, which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). In this paper, we investigate its extension, called supervised locality pursuit embedding (SLPE), using class labels of data points to enhance its discriminant power in their mapping into a low dimensional space. We compare the proposed SLPE approach with traditional LPE, PCA and LDA methods on real-world data sets including handwritten digits, character data set and face images. Experimental results demonstrate that SLPE is superior to other three methods in terms of recognition accuracy.