Manifold Alignment with Multi-graph Embedding

Abstract

In this paper, a novel manifold alignment approach via multi-graph embedding (MA-MGE) is proposed. Different from the traditional manifold alignment algorithms that use a single graph to describe the latent manifold structure of each dataset, our approach utilizes multiple graphs for modeling multiple local manifolds in multi-view data alignment. Therefore a composite manifold representation with complete and more useful information is obtained from each dataset through a dynamic reconstruction of multiple graphs. Experimental results on Protein and Face-10 datasets demonstrate that the mapping coordinates of the proposed method provide better alignment performance compared to the state-of-the-art methods, such as semi-supervised manifold alignment (SS-MA), manifold alignment using Procrustes analysis (PAMA) and manifold alignment without correspondence (UNMA).