%0 Journal Article %1 HintonSalakhutdinov2006b %A Hinton, G E %A Salakhutdinov, R R %D 2006 %J Science %K dimensionalityreduction neuralnetworks parameterestimation %N 5786 %P 504-507 %R 10.1126/science.1127647 %T Reducing the dimensionality of data with neural networks %U http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=16873662&cmd=showdetailview&indexed=google %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{HintonSalakhutdinov2006b, 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 = {2008-07-15T10:05:18.000+0200}, author = {Hinton, G E and Salakhutdinov, R R}, biburl = {https://www.bibsonomy.org/bibtex/2135bbce97b449ddf5fca7be88102b53c/tmalsburg}, description = {Reducing the dimensionality of data with neural ne...[Science. 2006] - PubMed Result}, doi = {10.1126/science.1127647}, interhash = {019918b82518b74f443a22dc58a0117f}, intrahash = {135bbce97b449ddf5fca7be88102b53c}, journal = {Science}, keywords = {dimensionalityreduction neuralnetworks parameterestimation}, month = Jul, number = 5786, pages = {504-507}, pmid = {16873662}, timestamp = {2008-07-15T10:05:18.000+0200}, title = {Reducing the dimensionality of data with neural networks}, url = {http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=16873662&cmd=showdetailview&indexed=google}, volume = 313, year = 2006 }