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We adapt concepts, methodology, and theory originally developed in the areasof multidimensional scaling and dimensionality reduction for multivariate datato the functional setting. We focus on classical scaling and Isomap --prototypical methods that have played important roles in these area -- andshowcase their use in the context of functional data analysis. In the process,we highlight the crucial role that the ambient metric plays.

Embedding Functional Data: Multidimensional Scaling and Manifold Learning