Reducing the Dimensionality of Data with Neural Networks

DOI Web Site 351 Citations
  • G. E. Hinton
    Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario M5S 3G4, Canada.
  • R. R. Salakhutdinov
    Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario M5S 3G4, Canada.

Description

<jats:p>High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such “autoencoder” networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.</jats:p>

Journal

  • Science

    Science 313 (5786), 504-507, 2006-07-28

    American Association for the Advancement of Science (AAAS)

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