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Notion of information and independent component analysis [PDF]
Applications of Mathematics, 2020 Partial orderings and measures of information for continuous univariate random variables with special roles of Gaussian and uniform distributions are discussed. The information measures and measures of non-Gaussianity including third and fourth cumulants are generally used as projection indices in the projection pursuit approach for the independent ...
Radojičić, Una +2 more
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Topographic Independent Component Analysis [PDF]
Neural Computation, 2001 In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated “independent” components are often not at all independent. We propose that this residual dependence structure could be used to define a topo-graphic
Aapo Hyvärinen +2 more
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Kernel independent component analysis [PDF]
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004 Summary: We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On
Francis R. Bach, Michael I. Jordan
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Randomized independent component analysis [PDF]
2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016 Independent component analysis (ICA) is a method for recovering statistically independent signals from observations of unknown linear combinations of the sources. Some of the most accurate ICA decomposition methods require searching for the inverse transformation which minimizes different approximations of the Mutual Information, a measure of ...
Matan Sela, Ron Kimmel
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Independent component analysis of textures [PDF]
Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999 A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on independent component analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product ...
Roberto Manduchi, Javier Portilla
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Principal independent component analysis [PDF]
IEEE Transactions on Neural Networks, 1999 Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available.
Jie Luo 0001 +3 more
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Boosting Independent Component Analysis
IEEE Signal Processing Letters, 2022 Independent component analysis is intended to recover the mutually independent components from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. To alleviate the dependency on prior knowledge concerning unknown sources, many nonparametric methods have been proposed.
YunPeng Li 0003, Zhaohui Ye
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Egyptian Journal of Remote Sensing and Space Sciences, 2021 The main objective of this research is to evaluate change detection techniques to monitoring land-cover changes that occurred between 2016 and 2017 in the study area located in new administrative capital region in Cairo Governorate, Egypt. The Study area
Ahmed Saber +3 more
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Multiview Independent Component Analysis with Delays
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023 Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the presence of multiple views of the same sources can be used. In this work, we present MultiView Independent Component
Heurtebise, Ambroise +2 more
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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), 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Seungjin Choi +2 more
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