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Joint Diagonalization of Kernels for Information Fusion

  • Alberto Muñoz
  • Javier González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

Information Fusion is becoming increasingly relevant in fields such as Image Processing or Information Retrieval. In this work we propose a new technique for information fusion when the sources of information are given by a set of kernel matrices. The algorithm is based on the joint diagonalization of matrices and it produces a new data representation in an Euclidean space. In addition, the proposed method is able to eliminate redundant information among the input kernels and it is robust against the presence of noisy variables and irrelevant kernels.

The performance of the algorithm is illustrated on data reconstruction and classifications problems.

Keywords

Information Fusion Approximate Joint Diagonalization Kernel Methods Support Vector Machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Alberto Muñoz
    • 1
  • Javier González
    • 1
  1. 1.Universidad Carlos III de Madrid, c/ Madrid 126, 28903 GetafeSpain

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