Neural networks which identify composite factors
We investigate the use of an artificial neural network to form a sparse distributed representation of the underlying factors in data sets. We extend the previously proposed [1] network so that it may identify composite causes in data sets by creating a hierarchical network. We use the network as a means of identifying individual faces when the network is trained on a mixture of faces and show both analytically and through experiments how noise allows us to find precisely the factors without prior assumptions of the number of factors.