Corpus ID: 220514200

Extendable and invertible manifold learning with geometry regularized autoencoders

@article{Duque2020ExtendableAI,
  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},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.07142}
}
  • 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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    References

    SHOWING 1-10 OF 54 REFERENCES
    Laplacian Auto-Encoders: An explicit learning of nonlinear data manifold
    • 39
    • Highly Influential
    Embedding with Autoencoder Regularization
    • 32
    • PDF
    Topological Autoencoders
    • 9
    • PDF
    Locally Linear Landmarks for Large-Scale Manifold Learning
    • 22
    • PDF
    Extracting and composing robust features with denoising autoencoders
    • 4,156
    • Highly Influential
    • PDF
    UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
    • 1,386
    • PDF
    Nonlinear Dimensionality Reduction
    • 1,247
    • PDF
    A global geometric framework for nonlinear dimensionality reduction.
    • 9,550
    • Highly Influential
    • PDF
    Auto-Encoding Variational Bayes
    • 9,776
    • PDF