Orthogonal Neighborhood Preserving Embedding Based Dimension Reduction and Classification Method
正交化近邻关系保持的降维及分类算法

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.