An autoencoder is a feedforward neural network that aims to learn how to compress the original dataset. Its aim is to copy input to its output. Therefore, instead of mapping features to the input layer and labels to the output layer, we will map the features to both the input and output layers. The number of units in the hidden layers is usually different from the number of units in the input layers, which forces the network to either expand or reduce the number of original features. This way, the network will learn the important features, while effectively applying dimensionality reduction.
An example network is shown in the following diagram. The three-unit input layer is first expanded into a four-unit layer and then compressed ...
