Reducing the Dimensionality of Data with Neural Networks

Reducing the Dimensionality of Data with Neural Networks. Hinton, G. E. & Salakhutdinov, R. R. Science, 313(5786):504–507, July, 2006. rate: 5

Paper doi abstract bibtex 14 downloads

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.

@article{hinton_reducing_2006,
	title = {Reducing the {Dimensionality} of {Data} with {Neural} {Networks}},
	volume = {313},
	issn = {0036-8075, 1095-9203},
	url = {https://www.science.org/doi/10.1126/science.1127647},
	doi = {10.1126/science.1127647},
	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.},
	language = {en},
	number = {5786},
	urldate = {2023-06-16},
	journal = {Science},
	author = {Hinton, G. E. and Salakhutdinov, R. R.},
	month = jul,
	year = {2006},
	note = {rate: 5},
	keywords = {\#Science, /unread, ⭐⭐⭐⭐⭐},
	pages = {504--507},
}

Downloads: 14