A novel algorithm for computing rank reduced matrix approximations

The problem of approximating a matrix by one of lower rank occurs in a number of signal processing problems, including reduced rank Wiener filtering and reduced rank maximum likelihood estimation. This paper derives a novel algorithm for computing the optimal rank-reduced approximation of a given matrix under an arbitrary weighted norm. Simulations demonstrate the advantages of the algorithm over the traditional alternating projections algorithm.