The Effect of Mutual Information on Independent Component Analysis in EEG/MEG Analysis: A Simulation Study

2008 ◽  
Vol 118 (11) ◽  
pp. 1534-1546 ◽  
Author(s):  
A. Neumann ◽  
M. Grosse-Wentrup ◽  
M. Buss ◽  
K. Gramann
Keyword(s):  
Mutual Information ◽  
Independent Component Analysis ◽  
Simulation Study ◽  
Component Analysis ◽  
Independent Component

scholarly journals Robust Independent Component Analysis Based on Two Scatter Matrices

2016 ◽  
Vol 37 (1) ◽  
Author(s):  
Klaus Nordhausen ◽  
Hannu Oja ◽  
Esa Ollila
Keyword(s):  
Independent Component Analysis ◽  
Covariance Matrix ◽  
Simulation Study ◽  
Component Analysis ◽  
Independent Component ◽  
Scatter Matrix ◽  
Independent Components

Oja, Sirkiä, and Eriksson (2006) and Ollila, Oja, and Koivunen (2007) showed that, under general assumptions, any two scatter matrices with the so called independent components property can be used to estimate the unmixing matrix for the independent component analysis (ICA). The method is a generalization of Cardoso’s (Cardoso, 1989) FOBI estimate which uses the regular covariance matrix and a scatter matrix based on fourth moments. Different choices of the two scatter matrices are compared in a simulation study. Based on the study, we recommend always the use of two robust scatter matrices. For possible asymmetric independent components, symmetrized versions of the scatter matrix estimates should be used.

scholarly journals Minimax Mutual Information Approach for Independent Component Analysis

2004 ◽  
Vol 16 (6) ◽  
pp. 1235-1252 ◽  
Author(s):  
Deniz Erdogmus ◽  
Kenneth E. Hild ◽  
Yadunandana N. Rao ◽  
José C. Príncipe
Keyword(s):  
Mutual Information ◽  
Independent Component Analysis ◽  
Maximum Entropy ◽  
Component Analysis ◽  
Performance Measure ◽  
Higher Order ◽  
Independent Component ◽  
Maximization Problem ◽  
Maximum Output ◽  
Ica Algorithm

Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information-theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estimate into the mutual information expression, and in the latter we incorporate the source pdf assumption in the algorithm through the use of nonlinearities matched to the corresponding cumulative density functions (cdf). Alternative solutions to ICA use higher-order cumulant-based optimization criteria, which are related to either one of these approaches through truncated series approximations for densities. In this article, we propose a new ICA algorithm motivated by the maximum entropy principle (for estimating signal distributions). The optimality criterion is the minimum output mutual information, where the estimated pdfs are from the exponential family and are approximate solutions to a constrained entropy maximization problem. This approach yields an upper bound for the actual mutual information of the output signals—hence, the name minimax mutual information ICA algorithm. In addition, we demonstrate that for a specific selection of the constraint functions in the maximum entropy density estimation procedure, the algorithm relates strongly to ICA methods using higher-order cumulants.

Soft sensor modelling of propylene conversion based on a Takagi-Sugeno fuzzy neural network optimized with independent component analysis and mutual information

2018 ◽  
Vol 41 (3) ◽  
pp. 737-748 ◽  
Author(s):  
Shuting Liu ◽  
Xianwen Gao ◽  
Wenhai Qi ◽  
Shumei Zhang
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Independent Component Analysis ◽  
Fuzzy Neural Network ◽  
Component Analysis ◽  
Soft Sensor ◽  
Independent Component ◽  
Fuzzy Neural ◽  
Propylene Conversion ◽  
Sensor Modelling

Propylene conversion is important to economic efficiency in the production of acrylic acid. Hence, the online measurement of propylene conversion is becoming more and more important. The current measurement method is mainly uses an offline meteorological chromatography analyser, which is difficult to measure accurately in real time. A soft sensor modelling method of propylene conversion based on Takagi-Sugeno (T-S) fuzzy neural network optimized by independent component analysis and mutual information is proposed in this paper. Firstly, fast independent component analysis-based denoising strategy is developed to remove the noise in the measurement of variables influenced by propylene conversion. Then, a mutual information-based variable selection method is proposed to select the key variables from multitudinous variables to reduce the influence of weak correlation. Finally, a T-S fuzzy neural network algorithm is employed to forecast the propylene conversion in the process of propylene oxidation. Simulation results show that the proposed soft sensor modelling method has better prediction accuracy and generalization ability. The method of this paper is obvious and effective.

Constrained Subspace ICA Based on Mutual Information Optimization Directly

2008 ◽  
Vol 20 (4) ◽  
pp. 964-973 ◽  
Author(s):  
Marc M. Van Hulle
Keyword(s):  
Mutual Information ◽  
Independent Component Analysis ◽  
Gradient Descent ◽  
Edgeworth Expansion ◽  
Component Analysis ◽  
Independent Component ◽  
New Approach ◽  
Robust Version ◽  
Constrained Independent Component Analysis ◽  
Information Optimization

We introduce a new approach to constrained independent component analysis (ICA) by formulating the original, unconstrained ICA problem as well as the constraints in mutual information terms directly. As an estimate of mutual information, a robust version of the Edgeworth expansion is used, on which gradient descent is performed. As an application, we consider the extraction of both the mother and the fetal antepartum electrocardiograms (ECG) from multielectrode cutaneous recordings on the mother's thorax and abdomen.

Nonnegative independent component analysis based on minimizing mutual information technique

2006 ◽  
Vol 69 (7-9) ◽  
pp. 878-883 ◽  
Author(s):  
Chun-Hou Zheng ◽  
De-Shuang Huang ◽  
Zhan-Li Sun ◽  
Michael R. Lyu ◽  
Tat-Ming Lok
Keyword(s):  
Mutual Information ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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