应用于数据降维的Laplacian 特征映射增量学习算法研究

he traditional nonlinear manifold learning methods have achieved great success in dimensionality reduction. However, when new samples are observed, the batch methods fail to learn them incrementally. This paper presents out-of-sample extension for Laplacian Eigenmaps, which computes the low-dimensional representation of data set by optimally preserving local neighborhood information in a certain sense. Two different incremental algorithms, the differential method and sub-manifold analysis method, are proposed. The algorithms are easy to be implemented and the computation procedure is simple. Simulation results testify the efficiency and accuracy of the proposed algorithm.