Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Signal Processing - Special issue: Information theoretic signal processing
Blind deconvolution in nonminimum phase systems using cascade structure
EURASIP Journal on Applied Signal Processing
Temporal and spatial features of single-trial EEG for brain-computer interface
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
On entropy rate for the complex domain and its application to i.i.d. sampling
IEEE Transactions on Signal Processing
Blind separation of digital signal sources in noise circumstance
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Contrast functions for independent subspace analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Hi-index | 754.84 |
This paper presents a unified approach to the problem of blind separation of sources, based on the concept of mutual information. This concept is applied to the whole source sequences as stationary processes and thus provides a universal contrast applicable to both the instantaneous and convolutive mixture cases. For practical implementation, we introduce several degraded forms of this contrast, computable from a finite-dimensional distribution of the reconstructed source processes only. From them, we derive several sets of estimating equations, generalizing those considered earlier