Adaptive blind signal and image processing

By Andrzej S. Cichocki, Shun-ichi Amari. 2002

1,499 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f5fc40d95594553940672c5f2ce054a626898acb

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Information theoretical estimators toolbox (2014, background): “…multi-information when two variables are considered. c©2014 Zoltán Szabó. the ISA separation principle (Cardoso, 1998): the ISA task can be solved by ICA (ISA with one-dimensional sources, Hyvärinen et al. 2001; Cichocki and Amari 2002; Choi et al. 2005) followed by clustering of the ICA elements.”
A robustification of independent component analysis (2005, methodology): “Here we attempt to unmix four images [23] which were obtained by mixing gray images by an orthogonal mixing matrix.”
Determination of rice type by 1H NMR spectroscopy in combination with different chemometric tools (2014, background): “This also explains the fact that the best models were found in different combinations of ICs/PCs (IC1–IC2 and PC2–PC3).”
Independent component analysis: algorithms and applications (2000, background): “Independent Component Analysis (ICA); see Hyvärinen, Karhunen, and Oja (2001) and Cichocki and Amari (2002) is a novel statistical signal and data analysis method. It assumes a statistical model whereby the observed multivariate data x, typically given as a large database of samples, are assumed to ”
Sparse component analysis and blind source separation of underdetermined mixtures (2005, background): “There is a large amount of papers devoted to ICA problems [2], [5] but mostly for the case m n.”
Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “…Component Analysis (SCA), Sparse PCA (SPCA), Non-negative Matrix Factorization (NMF), Smooth Component Analysis (SmoCA), Parallel Factor Analysis (PARAFAC), Time-Frequency Component Analyzer (TFCA) and Multichannel Blind Deconvolution (MBD) [4] [40] [78] [13] [90] [149] [150] [36] [95] [108].”
Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization (2007, methodology): “The basic approach to NMF is alternating minimization or alternating projection: the specified cost function is alternately minimized with respect to two sets of the parameters {sjt} and {aij}, each time optimizing one set of arguments while keeping the other one fixed [9,1].”
Csiszár's Divergences for Non-negative Matrix Factorization: Family of New Algorithms (2006, methodology): “The basic approach to NMF is alternating minimization or alternating projection: the specified loss function is alternately minimized with respect to two sets of parameters {xjk} and {aij}, each time optimizing one set of arguments while keeping the other one fixed [2, 3, 8].”
Subtracting a best rank-1 approximation may increase tensor rank (2009, background): “In some applications, tensors may be symmetric only in some modes [7], or may not be symmetric nor have equal dimensions [3] [22] [19].”
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014, background): “Multi-faceted data can be naturally stacked into a tensor; for instance, in wire-5 less communications the so-called signal diversity (temporal, spatial, spectral, . . . ) corresponds to the order of the tensor [18].”
Bilevel approaches for learning of variational imaging models (2015, methodology): “…under-sampled measurements, for instance, has been attempted in engineering and statistics (in particular signal processing) using ﬁlters [60, 73, 28] and multi scale analysis [79, 50, 80], in statistical inverse problems using Bayesian inversion and machine learning [37] and in mathematical…”
Kernel Methods for Measuring Independence (2005, methodology): “…of instantaneous independent component analysis(ICA), drawing on the numerous existing surveys of ICA and related methods, including those by Hyvärinen et al. (2001); Lee et al. (2000); Cichocki and Amari (2002); Haykin (1998); as well as the review by Comon (1994) of older literature on the topic.”
Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment (2011, background): “If we have a sufficient number of microphones , i.e., a determined case, linear filters that are estimated for example by independent component analysis (ICA) [1]–[4] effectively separate the mixtures.”