  The nonnegative matrix factorization is a widely used, flexible matrix
decomposition, finding applications in biology, image and signal processing and
information retrieval, among other areas. Here we present a related matrix
factorization. A multi-objective optimization problem finds conical
combinations of templates that approximate a given data matrix. The templates
are chosen so that as far as possible only the initial data set can be
represented this way. However, the templates are not required to be nonnegative
nor convex combinations of the original data.
