Clustering noisy data by a principal feature extraction unsupervised neural network
Francesco Vacca, E. Chiarantoni · 2002
Principal feature classification is based on a sequential procedure for finding the principal features from an assigned data set. This paper presents an unsupervised neural network which is able to find principal features, based on neural units sensitive to density of the data space. These units adopt a modified competitive learning law, which utilizes only local information to specialize toward a single cluster. It is shown that the network presented is able to automatically select the number of units as in the rival penalized competitive network, and also to correctly detect features when the number of clusters exceed the number of units. Simulations on IRIS data set are provided and it is shown that the proposed network presents property of robust noise rejection and is suitable for features extraction in noise data sets.