AIP Conference Proceedings 151 on Neural Networks for Computing
A neural net for blind separation of nonstationary signals
Neural Networks
Natural gradient works efficiently in learning
Neural Computation
Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Equivariant nonstationary source separation
Neural Networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
| Hi-index | 0.00 |
In this paper, I introduce a concept of differential decorrelation which finds a linear mapping that minimizes the concurrent change of variables. Motivated by the differential anti-Hebbian rule [1], I develop a natural gradient algorithm for differential decorrelation and present its local stability analysis. The algorithm is successfully applied to the task of nonstationary source separation