A Study on Various Dimensionality Reduction Techniques Applied in the General Shape Analysis

  • Katarzyna Gościewska
  • Dariusz FrejlichowskiEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 342)


The paper presents selected numerical data dimensionality reduction techniques and their application to reduce the size of feature vectors used in an exemplary General Shape Analysis (GSA) task. The usability of applying reduced feature vectors was experimentally tested using three Fourier transform-based shape descriptors and three data reduction approaches. The aim of the experiments was to investigate which data reduction approach is the best, i.e., gives the highest percentage effectiveness value while maintaining a minimal size of the feature vector.


General shape analysis Fourier descriptors Feature dimensionality reduction 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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