Neural networks for blind separation with unknown number of sources

Blind source separation problems have recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the number of sources is typically assumed to be known in advance, but this does not usually hold in practical applications. In this paper, various neural network architectures and associated adaptive learning algorithms are discussed for handling the cases where the number of sources is unknown. These techniques include estimation of the number of sources, redundancy removal among the outputs of the networks, and extraction of the sources one at a time. Validity and performance of the described approaches are demonstrated by extensive computer simulations for natural image and magnetoencephalographic (MEG) data. (C) 1999 Elsevier Science B.V. All rights reserved.