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An Efficient Optimization Method for Revealing Local Optima of Projection Pursuit Indices

  • Souad Larabi Marie-Sainte
  • Alain Berro
  • Anne Ruiz-Gazen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)

Abstract

In order to summarize and represent graphically multidimensional data in statistics, projection pursuit methods look for projection axes which reveal structures, such as possible groups or outliers, by optimizing a function called projection index. To determine these possible interesting structures, it is necessary to choose an optimization method capable to find not only the global optimum of the projection index but also the local optima susceptible to reveal these structures. For this purpose, we suggest a metaheuristic which does not ask for many parameters to settle and which provokes premature convergence to local optima. This method called Tribes is a hybrid Particle Swarm Optimization method (PSO) based on a stochastic optimization technique developed in [2]. The computation is fast even for big volumes of data so that the use of the method in the field of projection pursuit fulfills the statistician expectations.

Keywords

Particle Swarm Optimization Local Optimum Good Particle Projection Pursuit Interesting Structure 
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 2010

Authors and Affiliations

  • Souad Larabi Marie-Sainte
    • 1
  • Alain Berro
    • 1
  • Anne Ruiz-Gazen
    • 2
  1. 1.IRIT-UT1, UMR 5505, CNRSUniversité Toulouse 1 - CapitoleToulouseFrance
  2. 2.Toulouse School of Economics (GREMAQ)Université Toulouse 1 - CapitoleToulouseFrance

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