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Extendable and invertible manifold learning with geometry regularized autoencoders
Our regularization, based on the diffusion potential distances from the recently-proposed PHATE visualization method, encourages the learned latent representation to follow intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and reconstruction of data in the original feature space from latent coordinates.
Visualizing High Dimensional Dynamical Processes
Manifold learning techniques for dynamical systems and time series have shown their utility for a broad spectrum of applications in recent years.
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