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Independent component analysis (ICA) has proven useful for the analysis of functional magnetic resonance imaging (fMRI) data. In this paper, we compare the performance of three ICA algorithms and show the importance of taking sample correlation information into account. The three ICA algorithms are Infomax, the most widely used algorithm for fMRI analysis, entropy bound minimization (EBM) that adapts...
Estimation of the order of functional magnetic resonance imaging (fMRI) data is a crucial step in data-driven methods assuming a multivariate linear model. Use of information theoretic criteria for model order detection was proven useful but the sample dependence in fMRI data limits this usefulness. In this paper, we propose an iterative procedure that jointly estimates the downsampling depth and...
The commonly used principal component analysis (PCA) assumes circular Gaussian distribution for the observed complex random variables. This paper extends PCA to the general case where the signals can be noncircular, and introduces a new PCA method called the noncircular PCA (ncPCA). We study the properties of ncPCA and propose an efficient algorithm for its implementation. Numerical results are presented...
It has been shown that using minimum error entropy as the cost function leads to important performance gains in adaptive filtering, especially when the Gaussianity assumptions on the error distribution do not hold. In this paper, we show that by using the entropy bound rather than the entropy, we can derive an efficient algorithm for supervised training. We demonstrate its effectiveness by a system...
A novel (differential) entropy estimator is introduced where the maximum entropy bound is used to approximate the entropy given the observations, and is computed using a numerical procedure thus resulting in accurate estimates for the entropy. We show that such an estimator exists for a wide class of measuring functions, and provide a number of design examples to demonstrate its flexible nature. We...
We present a new (differential) entropy estimator where the maximum entropy bound is used to approximate the entropy given the observations, and is computed using a numerical procedure. The resulting accurate estimate for the entropy is used to derive a new algorithm to perform independent component analysis (ICA). The new algorithm, ICA by entropy bound minimization (ICA-EBM), adopts a line search...
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