Algorithm

Principal (computer security)

Materials science

Principal component analysis

High dimensional

Layer (electronics)

Curse of dimensionality

Gradient descent

Operating system

Computer science

Initialization

Nanotechnology

Pattern recognition (psychology)

Autoencoder

Artificial intelligence

Programming language

Artificial neural network

Science

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.

Reducing the Dimensionality of Data with Neural Networks