Presented is an algorithm for nonlinear dimensionality reduction that uses both local(short) and global(long) distances in order to learn the intrinsic geometry of manifolds with complicated topology. Since our algorithm matches nonlocal structures, it is robust even to strong noise. We show experimental results on both synthetic and real data demonstrating the advantages of our approach over stateof- the-art manifold learning methods.
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Rosman, G., Bronstein, A.M., Bronstein, M.M., Kimmel, R. (2008). Topologically Constrained Isometric Embedding. In: Rosenhahn, B., Klette, R., Metaxas, D. (eds) Human Motion. Computational Imaging and Vision, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6693-1_10
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