Results 11 to 20 of about 4,416,987 (284)
A Tutorial on Independent Component Analysis [PDF]
CoRR, 2014 Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning.
Shlens, Jonathon
core +2 more sources
Heavy-tailed Independent Component Analysis [PDF]
2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 2015 Independent component analysis (ICA) is the problem of efficiently recovering a matrix $A \in \mathbb{R}^{n\times n}$ from i.i.d. observations of $X=AS$ where $S \in \mathbb{R}^n$ is a random vector with mutually independent coordinates. This problem has
Anderson, Joseph +3 more
core +2 more sources
Binary Independent Component Analysis with OR Mixtures [PDF]
IEEE Transactions on Signal Processing, 2010 Independent component analysis (ICA) is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non-Gaussian source signals.
Nguyen, Huy, Zheng, Rong
core +2 more sources
Noisy independent component analysis of auto-correlated components [PDF]
Physical Review E, 2017 We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and ...
Enßlin, Torsten A., Knollmüller, Jakob
core +4 more sources
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
openaire +3 more sources
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +2 more sources
Independent nonlinear component analysis [PDF]
Journal of the American Statistical Association, 2019 The idea of summarizing the information contained in a large number of variables by a small number of "factors" or "principal components" has been broadly adopted in economics and statistics. This paper introduces a generalization of the widely used principal component analysis (PCA) to nonlinear settings, thus providing a new tool for dimension ...
Gunsilius, Florian +1 more
openaire +2 more sources