Efficient isometric multi-manifold learning based on the self-organizing method

Geodesic distance, as an essential measurement for data similarity, has been successfully used in manifold learning. However, many geodesic based isometric manifold learning algorithms, such as the isometric feature mapping (Isomap) and GeoNLM, fail to work on data that distribute on clusters or multiple manifolds. This limits their applications because practical data sets generally distribute on multiple manifolds. In this paper, we propose a new isometric multi-manifold learning method called Multi-manifold Proximity Embedding (MPE) which can be efficiently optimized using the gradient descent method or the self-organizing method. Compared with the previous methods, the proposed method has two steps which can isometrically learn data distributed on several manifolds and is more accurate in preserving both the intra-manifold and the inter-manifold geodesic distances. The effectiveness of the proposed method in recovering the nonlinear data structure and clustering is demonstrated through experiments on both synthetically and real data sets.