Extendable and invertible manifold learning with geometry regularized autoencoders

A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, especially for the purpose of faithfully visualizing data in two or three dimensions. Common approaches to this task use kernel methods for manifold learning... (read more)

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