AMUSE: a new blind identification algorithm

By Lang Tong, Victor C. Soon, Y. Huang, R. Liu. 1990

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In Statistics and Probability

Citations in the corpus

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA (2021, background): “This simple case amounts to having mutual independence between the sequences Z≤Ti , which is a standard assumption in the ICA literature (Tong et al., 1990; Hyvarinen and Morioka, 2017; Klindt et al., 2021).”
Blind separation of nonstationary sources in noisy mixtures (2000, methodology): “The mixing matrix is estimated by pre-whitening the data followed by a unitary transformation which jointly diagonalize cumulant matrices [2] or time-delayed correlation matrices [5, 4, 1].”
Adaptive blind signal and image processing (2002, methodology): “It is also important to note, that instead of using the generalized eigenvalue decomposition, we can use the standard eigenvalue decomposition (EVD) or equivalently the singular value decomposition (SVD) in a two stage procedure described in detail below [1159].”
THE THREE EASY ROUTES TO INDEPENDENT COMPONENT ANALYSIS; CONTRASTS AND GEOMETRY (2001, background): “These three models have already been considered in the literature.”
Identification of slow molecular order parameters for Markov model construction. (2013, methodology): “However, there is a simple and eﬃcient alternative to this: Problem (14) can also be solved by solving two simple eigenvalue problems using the AMUSE algorithm [47].”
Data-Driven Model Reduction and Transfer Operator Approximation (2017, methodology): “4 The TICA coordinates can be computed using AMUSE5 [34] as shown in Algorithm 1.”
EEG artifact removal—state-of-the-art and guidelines (2015, unknown): “signals (AMUSE [95]) and the temporal decorrelation”
Blind separation of instantaneous mixtures of nonstationary sources (2001, background): “This is the case when each source sequence is a stationary (possibly Gaussian) process with non proportional spectra [10, 12 , 2] and when sources are non stationary processes [11, 13, 7, 8, 4]. In this paper, we derive objective functions from a simple non stationary model and introduce algorithms”
Advances in Nonlinear Blind Source Separation (2003, background): “If the source signals are not plain random variables but have a temporal structure, linear blind source separation c an be achieved by utilizing either temporal correlations [19] , [20] or nonstationarity [21], [22] under somewhat differen t assumptions; see [1], Chapter 18.”
Indeterminacy and identifiability of blind identification (1991, background): “Recently, there have been considerable research interests in the general case of blind identification problems on which no particular structural assumptions on A are imposed [15]-[ 19 ].”