Identification of linear stochastic systems via second- and fourth-order cumulant matching
IEEE Transactions on Information Theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical characterisation and modelling of SAR images
Signal Processing
Dimensionality Reduction in Higher-Order-Only ICA
SPWHOS '97 Proceedings of the 1997 IEEE Signal Processing Workshop on Higher-Order Statistics (SPW-HOS '97)
Automatic Target Classification " Experiments on the MSTAR SAR Images
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
The use of ICA in multiplicative noise
Neurocomputing
ICA in signals with multiplicative noise
IEEE Transactions on Signal Processing - Part I
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The existence of multiplicative noise greatly limits the applicability of independent component analysis (ICA), because it does not take into account the existence of the noise. This paper proposes a method to extend ICA to this kind of noisy environment, without any limitation in the nature of the sources or the noise. In order to do this, the statistical structure of a linear transformation of the noisy data is studied up to fourth order, and then this structure is used to find the inverse of the mixing matrix through the minimization of a cost function. The method designed is able to extract the mixing matrix and some statistical features of the noise and the sources, notably improving the performance of the standard ICA methods when the mixture is contaminated by multiplicative noise.