LDLE: Low Distortion Local Eigenmaps
Gal Mishne, University of California, San Diego
In this talk I will present Low Distortion Local Eigenmaps (LDLE), a "bottom-up" 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 subsets of 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.
Joint work with Dhruv Kohli and Alex Cloninger.