title: Reducing the dimensionality of data with neural networks.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario M5S 3G4, Canada. hinton@cs.toronto.edu
authors.forename: G E
authors.initials: GE
authors.lastname: Hinton
authors.forename: R R
authors.initials: RR
authors.lastname: Salakhutdinov