Enhanced Locality Preserving Projections

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data. In this paper, a new manifold learning algorithm, called Enhanced Locality Preserving Projections, to identify the underlying manifold structure of a data set. ELPP considers both the between-class scatter and the within-class scatter in the processing of manifold learning. Equivalently, the goal of ELPP is to preserve the within-class geometric structure, while maximizing the between-class distance. Different from Principal component analysis (PCA) that aims to find a linear mapping which preserves total variance by maximizing the trace of feature variance and the optimal mapping is the leading eigenvectors of the total variance matrix associated with the leading eigenvalues, While locality preserving projections(LPP)that is in favor of preserving the local structure of the data set. We choose proper dimension of subspace that detects the intrinsic manifold structure for classification tasks. Experimental results on UMIST face database showed ELPP can represent class separability and clustering performance better than LDA and MMC. Extensive experiments on face recognition show the effectiveness of the proposed ELPP method.