Constrained PCA techniques for the identification of common factors in data

We present an analysis of a constrained principal components analysis network that identifies the common factors in data sets in a manner similar to principal factor analysis. This network responds to the covariance of the input data (not both variance and covariance as in PCA) and so is resistant to noise and varying levels of power on the inputs. The network naturally lends itself to the sparse coding of data, however, by enforcing this sparseness further we are able to decipher dual components in data.