Normalized Laplacian based Optimal Locality Preserving Projection

In the past few years, the computer vision and pattern recognition community has witnessed a rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among these methods, locality preserving projection (LPP) is one of the most promising feature extraction techniques. Based on LPP, this paper proposes a novel feature extraction algorithm, Normalized Laplacian based Optimal Locality Preserving Projection (NL-OLPP). Optimal here means that the extracted features are statistically uncorrelated and orthogonal, which are desirable for pattern analysis applications. We compare the proposed NL-OLPP with LPP, Orthogonal Locality Preserving Projection (OLPP) and Uncorrelated Locality Preserving Projection (ULPP) on the public available data sets, FERET and CMU PIE data sets. Experimental results show that the proposed NL-OLPP achieves much higher recognition accuracies.