  We present Low Distortion Local Eigenmaps (LDLE), a manifold learning
technique which constructs a set of low distortion local views of a dataset in
lower dimension and registers them to obtain a global embedding. The local
views are constructed using the global eigenvectors of the graph Laplacian and
are registered using Procrustes analysis. The choice of these eigenvectors may
vary across the regions. In contrast to existing techniques, LDLE is more
geometric and can embed manifolds without boundary as well as non-orientable
manifolds into their intrinsic dimension.
