Dimensionality reduction on Anchorgraph with an efficient Locality Preserving Projection

Manifold learning based dimensionality reduction methods have been successfully applied in many pattern recognition tasks, due to their ability to well capture the underlying relationship between data points. These methods, however, meet some challenges in terms of the storage cost and the computation complexity with the rapidly increasing data size. We propose an improved dimensionality reduction algorithm called Anchorgraph-based Locality Preserving Projection (AgLPP), trying to cope with the limitations via a novel estimation of the relationship between data points. We extend AgLPP into a kernel version, and reformulate it into a novel sparse representation. The experiments on several real-world datasets have demonstrated the effectiveness and efficiency of our methods.