An Incremental Algorithm Based on K Nearest Neighbor Projection for Nonlinear Dimensionality Reduction

Recently, there are several algorithms to perform dimensionality reduction on low-dimensional nonlinear manifolds embedded in a high-dimensional space, such as ISOMAP, LLE, Laplacian eigenmaps, SPE and so on. Most of these techniques work in batch mode. In this paper, we present an incremental nonlinear dimensionality reduction algorithm based on the k nearest neighbor projection. The method can effectively map new data into the low-dimensional space by building a locally linear transformation model between the original space and the embedded space. Moreover, the algorithm can treat data set with noise. Experiments show that the algorithm proposed is effective and robust