Corpus ID: 173990775

Independent Component Analysis based on multiple data-weighting

  title={Independent Component Analysis based on multiple data-weighting},
  author={Andrzej Bedychaj and Przemysław Spurek and Lukasz Struski and Jacek Tabor},
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. In this paper we present Multiple-weighted Independent Component Analysis (MWeICA) algorithm, a new ICA method which is based on approximate diagonalization of weighted covariance matrices. Our idea is based on theoretical result, which says that linear independence of weighted data (for gaussian weights) guarantees independence… Expand
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Independent component analysis, A new concept?
  • P. Comon
  • Mathematics, Computer Science
  • Signal Process.
  • 1994
An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA). Expand
Fast and robust fixed-point algorithms for independent component analysis
  • A. Hyvärinen
  • Mathematics, Computer Science
  • IEEE Trans. Neural Networks
  • 1999
Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. Expand
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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 showExpand
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Tensorial extensions of independent component analysis for multisubject FMRI analysis
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An integrated approach to probabilistic independent component analysis for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise is presented and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses. Expand
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While spectral analysis only uses second-order properties of independent stochastic sources, a procedure based on higher-order analysis (fourth-order cross cumulants) is developed, which leads to a complete identification of the sources. Expand
Kernel ICA: An alternative formulation and its application to face recognition
The experiment using a subset of FERET database indicates that the proposed kernel ICA method significantly outperform ICA, PCA and KPCA in terms of the total recognition rate. Expand
Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis
Three independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) have been compared with other preprocessing methods in order to find out whether and to which extent spatialExpand