Uncorrelated Locality Preserving Projections

In this paper, we propose a new manifold learning algorithm, called Uncorrelated Locality Preserving Projections, to identify the underlying manifold structure of a data set. ULPP tries to find the subspace that best discriminates different face classes by maximizing the between-class distance, while minimizing the within-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 locality preserving projections(LPP) that is in favor of preserving the neighborhood structure of the data set. We choose proper dimension of subspace that detects the intrinsic manifold structure for classification tasks. Experiments comparing the proposed algorithm with some other popular algorithms on the JAFFE, AT&T, and Yale databases show that our algorithm consistently outperforms others.