Fast Projection Pursuit Based on Quality of Projected Clusters

  • Marek Grochowski
  • Włodzisław Duch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6594)


Projection pursuit index measuring quality of projected clusters (QPC) introduced recently optimizes projection directions by minimizing leave-one-out error searching for pure localized clusters. QPC index has been used in constructive neural networks to discover non-local clusters in high-dimensional multi-class data, reduce dimensionality, aggregate features, visualize and classify data. However, for n training instances such optimization requires O(n 2) calculations. Fast approximate version of QPC introduced here obtains results of similar quality with O(n) effort, as illustrated in a number of classification and data visualization problems.


Projection pursuit Classification Dimensionality reduction Naive Bayes Neural networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marek Grochowski
    • 1
  • Włodzisław Duch
    • 1
    • 2
  1. 1.Department of InformaticsNicolaus Copernicus UniversityToruńPoland
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore

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