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Data Ranking and Clustering via Normalized Graph Cut Based on Asymmetric Affinity

  • Olexiy Kyrgyzov
  • Isabelle Bloch
  • Yuan Yang
  • Joe Wiart
  • Antoine Souloumiac
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)

Abstract

In this paper, we present an extension of the state-of-the-art normalized graph cut method based on asymmetry of the affinity matrix. We provide algorithms for classification and clustering problems and show how our method can improve solutions for unequal and overlapped data distributions. The proposed approaches are based on the theoretical relation between classification accuracy, mutual information and normalized graph cut. The first method requires a priori known class labeled data that can be utilized, e.g., for a calibration phase of a brain-computer interface (BCI). The second one is a hierarchical clustering method that does not involve any prior information on the dataset.

Keywords

graph cut asymmetric affinity mutual information BCI 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Olexiy Kyrgyzov
    • 1
  • Isabelle Bloch
    • 1
  • Yuan Yang
    • 1
  • Joe Wiart
    • 2
  • Antoine Souloumiac
    • 3
  1. 1.Whist LabInstitut Mines-Telecom, ParisTech/CNRS LTCIParisFrance
  2. 2.Whist LabOrange Labs R&DIssy-les-MoulineauxFrance
  3. 3.LIST, Laboratoire Outils Analyse de DonnéesCEAGif-sur-YvetteFrance

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