Efficient Model Selection for Mixtures of Probabilistic PCA Via Hierarchical BIC

  • Jianhua Zhao
  • Published 2014 in IEEE Transactions on Cybernetics


This paper concerns model selection for mixtures of probabilistic principal component analyzers (MPCA). The well known Bayesian information criterion (BIC) is frequently used for this purpose. However, it is found that BIC penalizes each analyzer implausibly using the whole sample size. In this paper, we present a new criterion for MPCA called hierarchical… (More)
DOI: 10.1109/TCYB.2014.2298401


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