A Probability-Based Combination Method for Unsupervised Clustering with Application to Blind Source Separation

  • Julian Mathias Becker
  • Martin Spiertz
  • Volker Gnann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)


Unsupervised clustering algorithms can be combined to improve the robustness and the quality of the results, e.g. in blind source separation. Before combining the results of these clustering methods the corresponding clusters have to be aligned, but usually it is not known which clusters of the employed methods correspond to each other. In this paper, we present a method to avoid this correspondence problem using probability theory. We also present an application of our method in blind source separation. Our approach is better expandable than other state-of-the-art separation algorithms while leading to slightly better results.


Cluster Method Spectral Cluster Blind Source Separation Nonnegative Matrix Factorization Sound Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Julian Mathias Becker
    • 1
  • Martin Spiertz
    • 1
  • Volker Gnann
    • 1
  1. 1.Institut für NachrichtentechnikRWTH Aachen UniversityAachenGermany

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