A Corrected Criterion for Selecting the Optimum Number of Principal Components


  • Hannes Kazianka Institute of Statistics, University of Klagenfurt
  • Jürgen Pilz Institute of Statistics, University of Klagenfurt




Determining the optimum number of components to be retained is a key problem in principal component analysis (PCA). Besides the rule of thumb estimates there exist several sophisticated methods for automatically selecting the dimensionality of the data. Based on the probabilistic PCA model Minka (2001) proposed an approximate Bayesian model selection criterion. In this paper we correct this criterion and present a modified version. We compare the novel criterion with various other approaches in a simulation
study. Furthermore, we use it for finding the optimum number of principal components in hyper-spectral skin cancer images.


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How to Cite

Kazianka, H., & Pilz, J. (2016). A Corrected Criterion for Selecting the Optimum Number of Principal Components. Austrian Journal of Statistics, 38(3), 135–150. https://doi.org/10.17713/ajs.v38i3.268