Nonlinear Manifold Learning for Data Stream

There has been a renewed interest in understanding the structure of high dimensional data set based onmanifold learning. Examples include ISOMAP [25], LLE [20] and Laplacian Eigenmap [2] algorithms. Most of these algorithms operate in a “batch ” mode and cannot be applied efficiently for a data stream. We propose an incremental version of ISOMAP. Our experiments not only demonstrate the accuracy and efficiency of the proposed algorithm, but also reveal interesting behavior of the ISOMAP as the size of available data increases.