A robust generalization of isomap for new data

Most of existing nonlinear dimensionality reduction algorithms, such as isomap, LEE, Laplacian Eigenmaps, SPE and so on, do not provide a simple generalization to discover the low-dimensional embedding for new data points. In this paper, we present a robust extension for isomap to efficiently map new samples into the low-dimensional space. This generalization permits one to apply a trained model to new data points without having to recompute eigenvectors and can effectively treat data with noise. Two methods are used to estimate the geodesic distances between new data points and training points. Experimental results demonstrate that the proposed algorithm is effective.