Autoencoders are symmetrical neural networks which progressively compress their input down to a bottleneck, before expanding again to the starting dimensions. During training, an autoencoder aims to reproduce the output from the input and, in this process, it learns a compressed data representation at the bottleneck. Post-training, the structure up to the bottleneck, called the encoder, can be isolated to perform dimensionality reduction by forward pass . Autoencoders were only applied to the richer configurations, 'D', 'F' and 'G' (flattened) using an architecture of dense layers, and config 'G' specifically was also used in its 2D form to train a convolutional autoencoder (Table ). The number of parameters of all autoencoders is shown in Table . Caution is required when training autoencoders, as given enough model capacity it is possible for them to just learn the identity function between the input and output layer, i.e. to predict each feature of the output layer from the same feature in the input layer . This is a trivial mapping that would not reveal anything useful about the nature of the dataset. Regularisation in autoencoders aims to prevent this type of overfitting; however, the effectiveness of various regularisation strategies differs from other types of neural networks. Vincent et al. . showed that forcing an autoencoder to perform a denoising task as part of the reconstruction process can lead to better learned-representations. Noise can be added to the input layer in various forms. In this study we introduced noise by applying dropout to the input layer at a rate of 20%. This is equivalent to the addition of masking noise to the dataset, which was discussed by Vincent et al. Dropout was also applied to three additional layers of the encoder and decoder as shown in Table .