Open Access
Independent Component Analysis.
Seungjin Choi
- pp 435-459
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TLDR
The standardization of the IC model is talked about, and on the basis of n independent copies of x, the aim is to find an estimate of an unmixing matrix Γ such that Γx has independent components.About:
The article was published on 2012-01-01 and is currently open access. It has received 2296 citations till now. The article focuses on the topics: Independent component analysis.read more
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References
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Journal ArticleDOI
Robust neural networks with on-line learning for blind identification and blind separation of sources
Andrzej Cichocki,Rolf Unbehauen +1 more
TL;DR: Two unsupervised, self-normalizing, adaptive learning algorithms are developed for robust blind identification and/or blind separation of independent source signals from a linear mixture of them and are suitable for real-time implementations.
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Algorithms for nonnegative independent component analysis
TL;DR: It is sufficient to find the orthonormal rotation y=Wz of prewhitened sources z=Vx, which minimizes the mean squared error of the reconstruction of z from the rectified version y/sup +/ of y, which shows in particular the fast convergence of the rotation and geodesic methods.
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Learning the higher-order structure of a natural sound
TL;DR: How an Independent Component Analysis algorithm may be used to elucidate the higher-order structure of natural signals, yielding their independent basis functions, illustrated with the ICA transform of the sound of a fingernail tapping musically on a tooth.
Journal ArticleDOI
Robust learning algorithm for blind separation of signals
TL;DR: A novel, efficient, self-normalising, unsupervised adaptive learning algorithm for the on-line (real-time) separation of statistically independent unknown source signals from a linear mixture of them.