Embedding Functional Data: Multidimensional Scaling and Manifold Learning
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We adapt concepts, methodology, and theory originally developed in the areas of multidimensional scaling and dimensionality reduction for multivariate data to the functional setting. We focus on classical scaling and Isomap – prototypical methods that have played important roles in these area – and showcase their use in the context of functional data analysis. In the process, we highlight the crucial role that the ambient metric plays.
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