Blind separation of sources, Part I" An adaptive algorithm based on neuromimetic architecture

By Christian Jutten, J. Hérault. 2002

Subfields: Representation & Self-Supervised Learning, Signal Processing

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Citations in the corpus

Beyond CCA: Moment Matching for Multi-View Models (2016, background): “A notable identiﬁable version of FA is independent component analysis (ICA; Jutten, 1987; Jutten & H´erault, 1991; Comon & Jutten, 2010).”
Representation Learning: A Review and New Perspectives (2012, background): “Similarly, Le et al. (2011b) recently showed that adding a regularization term of the form ∑ t ∑ j s3(Wjx (t)) to a linear auto-encoder with tied weights, where s3 is a nonlinear convex function, yields an efficient algorithm for learning linear ICA.”
Deep learning in neural networks: An overview (2014, background): “d Hinton, 1994; Field, 1994; Hinton et al., 1995; Dayan and Zemel, 1995; Amari et al., 1996; Deco and Parra, 1997). Many do this to uncover and disentangle hidden underlying sources of signals (e.g., Jutten and Herault, 1991; Schuster, 1992; Andrade et al., 1993; Molgedey and Schuster, 1994; Comon, ”
One Big Net For Everything (2018, background): “…of q special output units and train them through unsupervised learning [62] to produce for t ≤ t T a vector code ( t ) ∈ R q that represents an ideal factorial code [2] of the observed history so far, or that encodes the data in related ways generally considered useful, e.g., [23, 28, 81, 68, 21].”
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals (2016, methodology): “…16 of 27 Ac ce pt ed M an us cr ip t 16 A nonlinear method of dimensionality reduction, ICA is a computational statistical technique that is very effective in disclosing the hidden factors underlying mixed samples of random variable measurements (Jutten and Herault, 1991; Martis et al., 2014).”
Klusters, NeuroScope, NDManager: a free software suite for neurophysiological data processing and visualization. (2006, methodology): “These include principal compoent analysis (PCA, Abeles and Goldstein, 1977), independent omponent analysis (ICA, Jutten and Herault, 1991), factor nalysis, and other related methods.”
Unsupervised image classification, segmentation, and enhancement using ICA mixture models (2002, background): “The goal of ICA is to linearly transform the data such that the transformed variables are as statistically independent from each other as possible [4], [11], [14], [23], [25].”
Advances in Nonlinear Blind Source Separation (2003, methodology): “Since (see [5] for a historical review and early references), several well-performing BSS and ICA algorithms [ 6 ], [7], [8], [9], [10], [11], [12] have been developed and applied to an increasing number of applications [13], [14], [15], [16], [17], [1], [18], [4].”
Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles (2012, methodology): “generated and the Independent Component Analysis (ICA), where a computational method for separating a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals [11].”
WordICA—emergence of linguistic representations for words by independent component analysis (2010, methodology): “In the classic version of the ICA model (Jutten and H´erault 1991; Comon 1994; Hyv¨arinen et al. 2001), each observed random variable is represented as a weighted sum of independent random variables.”
Discovering hierarchical speech features using convolutional non-negative matrix factorization (2003, background): “On the other hand, slow feature analysis (SFA) [2] tries to minimize the variance of features as the signal undergoes a transformation, and independent component analysis (ICA) [3] seeks to explain the data as a mixture of independent sources.”