Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Matrix computations (3rd ed.)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A globally convergent learning algorithm for PCA neural networks
Neural Computing and Applications
A Note on Stone's Conjecture of Blind Signal Separation
Neural Computation
Blind Source Separation Using Temporal Predictability
Neural Computation
Generalized weighted rules for principal components tracking
IEEE Transactions on Signal Processing
Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm
IEEE Transactions on Neural Networks
On blind separability based on the temporal predictability method
Neural Computation
Hi-index | 0.00 |
An efficient measure of signal temporal predictability is proposed, which is referred to as difference measure. We can prove that the difference measure of any signal mixture is between the maximal and minimal difference measure of the source signals. Previous blind source separation (BSS) problem is changed to a generalized eigenproblem by using Stone's measure. However, by using difference measure, the BSS problem is furthermore simplified to a standard symmetric eigenproblem. And the separation matrix is the eigenvector matrix, which can be solved by using principal component analysis (PCA) method. Based on difference measure, a few efficient algorithms have been proposed, which are either in batch mode or in on-line mode. Simulations show that difference measure is competitive with Stone's measure. Especially, the on-line algorithms derived from difference measure have better performance than those derived from Stone's measure.