|
|
ReportsReducing the Dimensionality of Data with Neural NetworksHigh-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. Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, Ontario M5S 3G4, Canada. * To whom correspondence should be addressed; E-mail: hinton{at}cs.toronto.edu
The editors suggest the following Related Resources on Science sites:In Science Magazine
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
|
Science. ISSN 0036-8075 (print), 1095-9203 (online)
Magazine | News | Signaling | Careers | Multimedia | Collections | Help | Site Map | RSS
Subscribe | Feedback | Privacy / Legal | About Us | Advertise With Us | Contact Us
© 2006 American Association for the Advancement of Science. All Rights Reserved.
AAAS is a partner of HINARI, AGORA, PatientInform, CrossRef, and COUNTER.
You have reached the bottom of the page. Back to top