%0 Journal Article %1 Hinton:2006 %A Hinton, G. E. %A Salakhutdinov, R. R. %D 2006 %J Science %K DNN RBM restricted-boltzmann-machine %P 504 - 507 %T Reducing the Dimensionality of Data with Neural Networks %V 313 %X 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 äutoencoder" 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.

@article{Hinton: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.}, added-at = {2018-02-01T12:07:07.000+0100}, author = {Hinton, G. E. and Salakhutdinov, R. R.}, biburl = {https://www.bibsonomy.org/bibtex/2ec11ecb333fe08f911354cd02e9e4908/geistgesicht}, description = {diverse cognitive systems bib}, interhash = {019918b82518b74f443a22dc58a0117f}, intrahash = {ec11ecb333fe08f911354cd02e9e4908}, journal = {Science}, keywords = {DNN RBM restricted-boltzmann-machine}, owner = {butz}, pages = {504 - 507}, timestamp = {2018-02-01T12:07:07.000+0100}, title = {Reducing the Dimensionality of Data with Neural Networks}, volume = 313, year = 2006 }