Uncorrelated Maximum Locality Preserving Projections

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features. In this paper, a new manifold learning algorithm, called uncorrelated maximum locality preserving projections(UMLPP), to identify the underlying manifold structure of a data set. UMLPP considers both the between-class scatter and the within-class scatter in the processing of manifold learning. Equivalently, the goal of UMLPP is to preserve the intrinsic graph characterizes the interclass compactness and connects each data point with its neighboring points of the same class. Different from principal component analysis (PCA) that aims to find a linear mapping which preserves total variance by maximizing the trace of feature variance, 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. Extensive experiments on face recognition demonstrate that the new feature extractors are effective, stable and efficient.