@article{ZieheKawanabeHarmeling2004,
  author    = {Ziehe, Andreas and Kawanabe, Motoaki and Harmeling, Stefan},
  title     = {Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation},
  issn      = {1532-4435},
  year      = {2004},
  abstract  = {We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm-a powerful technique from nonparametric statistics-to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearitics. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results},
  language  = {en}
}
@article{HarmelingMeineckeMueller2004,
  author    = {Harmeling, Stefan and Meinecke, Frank C. and M{\"u}ller, Klaus-Robert},
  title     = {Injecting noise for analysing the stability of ICA components},
  issn      = {0165-1684},
  year      = {2004},
  abstract  = {Usually, noise is considered to be destructive. We present a new method that constructively injects noise to assess the reliability and the grouping structure of empirical ICA component estimates. Our method can be viewed as a Monte-Carlo-style approximation of the curvature of some performance measure at the solution. Simulations show that the true root-mean-squared angle distances between the real sources and the source estimates can be approximated well by our method. In a toy experiment, we see that we are also able to reveal the underlying grouping structure of the extracted ICA components. Furthermore, an experiment with fetal ECG data demonstrates that our approach is useful for exploratory data analysis of real-world data. (C) 2003 Elsevier B.V. All rights reserved},
  language  = {en}
}
@phdthesis{Harmeling2004,
  author    = {Harmeling, Stefan},
  title     = {Independent component analysis and beyond},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0001540},
  school      = {Universit{\"a}t Potsdam},
  year      = {2004},
  abstract  = {'Independent component analysis' (ICA) ist ein Werkzeug der statistischen Datenanalyse und Signalverarbeitung, welches multivariate Signale in ihre Quellkomponenten zerlegen kann. Obwohl das klassische ICA Modell sehr n{\"u}tzlich ist, gibt es viele Anwendungen, die Erweiterungen von ICA erfordern. In dieser Dissertation pr{\"a}sentieren wir neue Verfahren, die die Funktionalit{\"a}t von ICA erweitern: (1) Zuverl{\"a}ssigkeitsanalyse und Gruppierung von unabh{\"a}ngigen Komponenten durch Hinzuf{\"u}gen von Rauschen, (2) robuste und {\"u}berbestimmte ('over-complete') ICA durch Ausreissererkennung, und (3) nichtlineare ICA mit Kernmethoden.},
  language  = {en}
}