Deep autoencoders

An autoencoder is used for feature selection and extraction. It consists of two symmetrical DBNs. The first half of the network is composed of several layers, which performs encoding. The second part of the network performs decoding. Each layer of the autoencoder is an RBM. This is illustrated in the following figure:

Deep autoencoders

The purpose of the encoding sequence is to compress the original input into a smaller vector space. The middle layer of the previous figure is this compressed layer. These intermediate vectors can be thought of as possible features of the dataset. The encoding is also referred to as the pre-training half. It is the ...

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