Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
A fast fixed-point algorithm for independent component analysis
Neural Computation
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Optimization by Vector Space Methods
Optimization by Vector Space Methods
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In independent component analysis problems, when we use a one-unit objective function to iteratively estimate several independent components, the uncorrelatedness between the independent components prevents them from converging to the same optimum. A simple and popular way of achieving decorrelation between recovered independent components is a deflation scheme based on a Gram-Schmidt-like decorrelation [7]. In this method, after each iteration in estimation of the current independent component, we subtract its 'projections' on previous obtained independent components from it and renormalize the result. Alternatively, we can use the constraints of uncorrelatedness between independent components to reduce the number of unknown parameters of the de-mixing matrix directly. In this paper, we propose to reduce the dimension of the de-mixing matrix to decorrelate different independent components. The advantage of this method is that the dimension reduction of the observations and de-mixing weight vectors makes the computation lower and produces a faster and efficient convergence.