Reducing the dimensionality of data with neural networks.


Resource | v1 | created by semantic-scholar-bot |
Type Paper
Created 2006-01-01
Identifier DOI: 10.1126/science.1127647

Description

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.

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