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Alignments of Manifold Sections of Different Dimensions in Manifold Learning

We consider an alignment algorithm for reconstructing global coordinates from local coordinates constructed for sections of manifolds. We show that, under certain conditions, the align- ment algorithm can successfully recover global coordinates even when local neighborhoods have different dimensions. Our results generalize an earlier analysis to allow alignment of sections of different dimensions. We also apply our result to a semisupervised learning problem.