@article{Hinton07282006,
author = {Hinton, G. E. and Salakhutdinov, R. R.},
title = {{Reducing the Dimensionality of Data with Neural Networks}},
journal = {Science},
volume = {313},
number = {5786},
pages = {504-507},
doi = {10.1126/science.1127647},
year = {2006},
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.
},
URL = {http://www.sciencemag.org/cgi/content/abstract/313/5786/504},
ee = {http://www.sciencemag.org/cgi/reprint/313/5786/504.pdf}
}