Abstract Manifold alignment has been found to be useful in many areas of machine learning and data mining. In this paper we introduce a novel manifold alignment approach, which differs from “semisupervised alignment” and “Procrustes alignment” in that it does not require predetermining correspondences. Our approach learns a projection that maps data instances (from two different spaces) to a lower dimensional space simultaneously matching the local geometry and preserving the neighborhood relationship within each set. This approach also builds connections between spaces de- fifined by different features and makes direct knowledge transfer possible. The performance of our algorithm is demonstrated and validated in a series of carefully designed experiments in information retrieval and bioinformatics