To overcome the sensitivity to the dimensions of reduced space

and performance degradation with wrong dimension estimation of neighborhood preserving embedding (NPE) method

an orthogonal neighborhood preserving embedding (ONPE) method is proposed for manifold dimension reduction. ONPE uses neighborhood information to construct the adjacent graph

and assuming that each data point can be represented by linear combination of its neighbor points. ONPE then extracts local geometry information embedded in reconstruction weights

and obtains the low dimensional coordinates by iteratively computes the mutually orthogonal basis functions. Moreover

utilizing the local geometry during ONPE dimension reduction

a new classification method (ONPC) based on a label propagation method (LNP) is proposed. The reasonable assumption is that local neighbor information in high dimensional space is also preserved in reduced space

and the class label of a data point can be obtained through the class labels of its neighbors. Several experiments on artificial datasets and face database demonstrate the effectiveness of the algorithm.