This is called dimensionality reduction. The two most common techniques for dimensionality reduction are using PCA (principal component analysis) and using a neural autoencoder. This article explains ...

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 ...