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- Alexander Kraskov, Harald Stögbauer, Peter Grassberger
- Physical review. E, Statistical, nonlinear, and…
- 2004

We present two classes of improved estimators for mutual information M(X,Y), from samples of random points distributed according to some joint probability density mu(x,y). In contrast to conventional… (More)

- Harald Stögbauer, Alexander Kraskov, Sergey A. Astakhov, Peter Grassberger
- Physical review. E, Statistical, nonlinear, and…
- 2004

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source… (More)

- Ralph G. Andrzejak, Alexander Kraskov, Harald Stögbauer, Florian Mormann, Thomas Kreuz
- Physical review. E, Statistical, nonlinear, and…
- 2003

The concept of surrogates allows testing results from time series analysis against specified null hypotheses. In application to bivariate model dynamics we here compare different types of surrogates,… (More)

– We present a conceptually simple method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and… (More)

Motivation: Clustering is a frequently used concept in variety of bioinformatical applications. We present a new method for hierarchical clustering of data called mutual information clustering (MIC)… (More)

- Sergey A. Astakhov, Harald Stögbauer, Alexander Kraskov, Peter Grassberger
- Analytical chemistry
- 2006

We propose a simulated annealing algorithm (stochastic non-negative independent component analysis, SNICA) for blind decomposition of linear mixtures of non-negative sources with non-negative… (More)

- Thomas Kreuz, Ralph G. Andrzejak, +5 authors Peter Grassberger
- Physical review. E, Statistical, nonlinear, and…
- 2004

In a growing number of publications it is claimed that epileptic seizures can be predicted by analyzing the electroencephalogram (EEG) with different characterizing measures. However, many of these… (More)

A recently proposed mutual information based algorithm for decomposing data into least dependent components (MILCA) is applied to spectral analysis, namely to blind recovery of concentrations and… (More)

We propose a simulated annealing algorithm (called SNICA for “stochastic non-negative independent component analysis”) for blind decomposition of linear mixtures of non-negative sources with… (More)

Obtaining the most independent components from a mixture (under a chosen model) is only the first part of an ICA analysis. After that, it is necessary to measure the actual dependency between the… (More)