Blind beamforming for non-gaussian signals

By Jean-François Cardoso, Antoine Souloumiac. 1993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Speech & Audio

Citations in the corpus

Beyond CCA: Moment Matching for Multi-View Models (2016, neutral): “…Appendix E.4) the spectral algorithm for NCCA (also with the generalized S-covariance matrices) to the JADE algorithm (the code is available at http://perso.telecom-paristech.fr/ car-doso/Algo/Jade/jadeR.m; Cardoso & Souloumiac, 1993) for independent component analysis (ICA) and to classical CCA.”
Rethinking LDA: Moment Matching for Discrete ICA (2015, neutral): “, [17, 18, 19, 20, 21, 5] and references therein) and machine learning (see [3, 4] and references therein) literature.”
Tensor decompositions for learning latent variable models (2012, methodology): “Related simultaneous diagonalization approaches have also been used for blind source separation and ICA (as discussed above), and a number of efficient algorithms have been developed for this problem (Bunse-Gerstner et al., 1993; Cardoso and Souloumiac, 1993; Cardoso, 1994; Cardoso and Comon, 1996; ”
Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “Since the cross-cumulants of independent signals are zero, one can obtain desired independent components by minimizing a cost function using the fourth order cross-cumulants or jointly diagonalizing eigenmatrices of the cross-cumulant tensor [26, 78].”
Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information (2012, background): “…seminal works of Geary (1942) and Reiersol (1950), a large number of authors (e.g., Kendall and Stuart 1979; Pal 1980; Kapteyn and Wansbeek 1983; Cardoso and Souloumiac 1993; Hyvärinen and Oja 1997; Cragg 1997; Lewbel 1997; Dagenais and Dagenais 1997; Erickson and Whited 2000; Ikeda and Toyama…”
Mining event-related brain dynamics. (2004, methodology): “ICA methods were introduced in France in the early 1990s [47, 48 ].”
Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “Since the cross-cumulants of independent signals are zero, one can obtain desired independent components by minimizing a cost function using the fourth order cross-cumulants or jointly diagonalizing eigenmatrices of the cross-cumulant tensor [26, 78].”
Multimodal Manifold Analysis by Simultaneous Diagonalization of Laplacians (2015, unknown): “Such methods have received limited attention in the numerical mathematics community [19] and in blind source separation applications [20], [21], [22], [23].”
Learning sparse filter bank transforms with convolutional ICA (2014, methodology): “Index Terms— independent component analysis, stationarity, sparsity, convolutional filters, fastICA, filter bank”
Efficient factor GARCH models and factor-DCC models (2009, methodology): “the non-Gaussianity ofyit; JADE ( Cardoso & Souloumiac, 1993 ) is based on joint diagonalization”
Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures (2007, methodology): “The simultaneous off-diagonalization can be realized by means of a simple variant of the Jacobi algorithm derived in [7], [8].”
Hyperspectral Image Classification With Independent Component Discriminant Analysis (2011, methodology): “Another approach for the estimation of the ICs is joint diagonalization of eigenmatrices (JADE) [42], which makes use of fourth-order cumulant tensors.”
A Linear Non-Gaussian Acyclic Model for Causal Discovery (2006, background): “Kawanabe and Müller (2005) provided estimating functions for JADE (Cardoso and Souloumiac, 1993) and for ICA based on non-Gaussianity maximization with orthogonality (uncorrelatedness) constraints including FastICA (Hyvärinen, 1999).”
Moment Component Analysis: An Illustration With International Stock Markets (2018, unknown): “As shown by Cardoso and Souloumiac (1993), this approach is equivalent to minimizing the sum of the squared fourth-order cross-cumulants of the whitened data, defined as∑ ijkl =iikl cum(zi,t , zj,t , zk,t , zl,t ) 2.”
High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality (2000, methodology): “In the last decade, an important alternative to PCA has been developed: ICA – independent components analysis [13, 2, 11].”
Fetal electrocardiogram extraction by blind source subspace separation (2000, methodology): “For example, in the Jacobi-type algebraic algorithms of [6], [8], [10] the multidimensional character of the sources limits the number of Jacobi-rotation angles that have to be identified, since rotations of the basis”
The design and analysis of information processing systems ] From Linear Adaptive Filtering to Nonlinear Information Processing (2009, methodology): “An extensive comparison of various algorithms in separating audio recordings was performed, and a summary of results obtained from the most popular and best-performing algorithms have been provided below, specifically, criterion (50) for Renyi’s quadratic entropy minimized using SIG (MRMI-SIG), same”