Separation of a mixture of independent signals using time delayed correlations.

By L. Molgedey, H. Schuster. 1994

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Citations in the corpus

Deep learning in neural networks: An overview (2014, neutral): “…Belouchrani, Abed-Meraim, Cardoso, & Moulines, 1997; Cardoso, 1994; Comon, 1994; Hyvärinen, Karhunen, & Oja, 2001; Jutten & Herault, 1991; Karhunen & Joutsensalo, 1995; Molgedey & Schuster, 1994; Schuster, 1992; Shan & Cottrell, 2014; Shan, Zhang, & Cottrell, 2007; Szabó, Póczos, & Lőrincz, 2006).”
An extension of slow feature analysis for nonlinear blind source separation (2014, methodology): “…is similar, ICA techniques that rely on higher order statistics (Bell and Sejnowski, 1995; Hyvärinen, 1999; Blaschke and Wiskott, 2004) may be favorable over second-oder techniques that rely on temporal correlations (Molgedey and Schuster, 1994; Belouchrani et al., 1997; Ziehe and Müller, 1998).”
Identification of slow molecular order parameters for Markov model construction. (2013, methodology): “Like PCA, TICA [55] uses a linear transform to map the original order parameters r ( t ) to a new set of order parameters z ( t ) — the independent components (ICs).”
Markov state models of biomolecular conformational dynamics (2014, unknown): “A major breakthrough followed in the exploitation of time-delayed correlation analysis, also called time-lagged or time-structure-based independent component analysis (tICA or TICA) [19].”
Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “Therefore, independent components can be obtained by diagonalizing the covariance matrices at the same time [62, 109, 111, 99].”
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “Among them, early approaches to ICA were based on time-delayed decorrelation [68], [225] or quadratic spatial contrasts [70], [226], which inspired ML-based approaches [53], [227].”
Information-based modeling of event-related brain dynamics. (2006, methodology): “These include JADE (Cardoso and Laheld, 1996), infomax ICA (Bell and Sejnowski, 1995), and so-called FastICA (Hyva¨rinen et al., 2001), as well as variants of second-order blind identification (SOBI) (Molgedey and Schuster, 1994) that also factor in relationships between multiple time points using a”
Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “Therefore, independent components can be obtained by diagonalizing the covariance matrices at the same time [62, 109, 111, 99].”
An Information-Maximization Approach to Blind Separation and Blind Deconvolution (1995, methodology): “In addition, Molgedey and Schuster (1994) proposed a novel technique that uses time-delayed correlations to constrain the solu-tion.”
VAMPnets for deep learning of molecular kinetics (2017, background): “…much fewer (typically 2-100) slow collective variables (CVs), often based on the variational approach or conformation dynamics [17, 18], time-lagged independent component analysis (TICA) [19, 20], blind source separation [21–23] or dynamic mode decomposition [24–28] – see [29, 30] for an overview.”