Abstract: A computer-implemented method of training an artificial neural network is provided. The artificial neural network comprises a feedforward autoencoder and a classification layer, the feedforward autoencoder comprising an input layer of neurons, an output layer of neurons, an embedding layer of neurons between the input and output layers, one or more non-linear intermediate layers of neurons between the input and embedding layer, one or more further non-linear intermediate layers of neurons between the embedding and output layer and respective network weights for each layer, wherein the number of neurons in the embedding layer is less than the number of neurons in the input layer. Data units are defined within a data space to have a data value for each dimension of the data space and a classification label associated with each data unit indicating one of a plurality of groups to which each data sample belongs.