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Flexible independent component analysis
Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378), 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Seungjin Choi +2 more
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Independent component analysis and beyond
Signal Processing, 2004Independent component analysis (ICA) aims at extracting unknown hidden factors/components from multivariate data using only the assumption that the unknown factors are mutually independent. Since the introduction of ICA concepts in the early 1980s in the context of neural networks and array signal processing, many new successful algorithms have been ...
Erkki Oja +2 more
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Multilinear Independent Components Analysis
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005Independent components analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging.
M. Alex O. Vasilescu +1 more
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Compressive Independent Component Analysis
2019 27th European Signal Processing Conference (EUSIPCO), 2019In this paper we investigate the minimal dimension statistic necessary in order to solve the independent component analysis (ICA) problem. We create a compressive learning framework for ICA and show for the first time that the memory complexity scales only quadratically with respect to the number of independent sources n, resulting in a vast ...
Michael P. Sheehan +2 more
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Research of independent component analysis
2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), 2005Independent component analysis (ICA) is a statistical technique to decompose multivariate data into statistically independent components. It could be applied to mine data of medical, economy or telecommunication systems, and to analyze data of GIS systems for agriculture or environment applications. To solve the problem of blind source separation, this
X. Yu +8 more
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Mathematics in independent component analysis
Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003This paper intends to give an overview of the author's PhD thesis entitled 'mathematics in independent component analysis', which has been finished in December 2002.
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Least-Squares Independent Component Analysis
Neural Computation, 2011Accurately evaluating statistical independence among random variables is a key element of independent component analysis (ICA). In this letter, we employ a squared-loss variant of mutual information as an independence measure and give its estimation method.
Suzuki, T., Sugiyama, M.
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Multidimensional independent component analysis
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002This paper proposes to generalize the notion of independent component analysis (ICA) to the notion of multidimensional independent component analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly parameterized in terms of one-dimensional subspaces.
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Independent component analysis: an introduction
Trends in Cognitive Sciences, 2002Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals.
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Applications of Independent Component Analysis
2004Blind source separation (BSS) is a computational technique for revealing hidden factors that underlie sets of measurements or signals. The most basic statistical approach to BSS is Independent Component Analysis (ICA). It assumes a statistical model whereby the observed multivariate data are assumed to be linear or nonlinear mixtures of some unknown ...
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