A Neural Network for PCA and Beyond

摘要

Principal Component Analysis (PCA) has been implemented by several neural methods. We discuss a Network which has previously been shown to find the Principal Component subspace though not the actual Principal Components themselves. By introducing a constraint to the learning rule (we do not allow the weights to become negative) we cause the same network to find the actual Principal Components. We then use the network to identify individual independent sources when the signals from such sources are ORed together.