Corpus ID: 220514200

Extendable and invertible manifold learning with geometry regularized autoencoders

  title={Extendable and invertible manifold learning with geometry regularized autoencoders},
  author={Andr'es F. Duque and Sacha Morin and Guy Wolf and Kevin R. Moon},
  • Andr'es F. Duque, Sacha Morin, +1 author Kevin R. Moon
  • Published 2020
  • Mathematics, Computer Science
  • ArXiv
  • 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. However, these methods typically only provide an embedding of fixed input data and cannot extend to new data points. On the other hand, autoencoders have recently become widely popular for representation… CONTINUE READING

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