An autoencoder, autoassociator or Diabolo network is an artificial neural network used for learning efficient codings. As such, it is part of the dimensionality reduction algorithms.
The aim of an auto-encoder is to learn a compressed, distributed representation (encoding) for a set of data. This means it is being used for dimensionality reduction. Auto-encoders use two or more layers, starting from the input data (for instance in a face recognition task this would be pixels in the photograph):
- A number of hidden layers (usually with a smaller number of neurons), which will form the encoder.
- A number of hidden layer leading to an output layer (usually progressively bigger until the last one, where each neuron has the same meaning as in the input layer), which will form the decoder.
If linear neurons are used, then the optimal solution to an auto-encoder is strongly related to PCA.
When the hidden layers are larger than the input layer, an autoencoder can potentially learn the identity function and become useless; however, experimental results have shown that such autoencoders might still serve learn useful features in this case.
Auto-encoders can also be used to learn overcomplete feature representations of data.
The "coding" is also known as the embedded space or latent space in dimensionality reduction where the encoder will be used to project and the decoder to reconstruct.
