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Bayesian Block-Diagonal Predictive Classifier for Gaussian Data

  • Jukka CoranderEmail author
  • Timo Koski
  • Tatjana Pavlenko
  • Annika Tillander
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 190)

Abstract

The paper presents a method for constructing Bayesian predictive classifier in a high-dimensional setting. Given that classes are represented by Gaussian distributions with block-structured covariance matrix, a closed form expression for the posterior predictive distribution of the data is established. Due to factorization of this distribution, the resulting Bayesian predictive and marginal classifier provides an efficient solution to the high-dimensional problem by splitting it into smaller tractable problems. In a simulation study we show that the suggested classifier outperforms several alternative algorithms such as linear discriminant analysis based on block-wise inverse covariance estimators and the shrunken centroids regularized discriminant analysis.

Keywords

Covariance estimators discriminant analysis high-dimensional data hyperparameters 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jukka Corander
    • 2
    Email author
  • Timo Koski
    • 1
  • Tatjana Pavlenko
    • 1
  • Annika Tillander
    • 3
  1. 1.KTH Royal Institute of TechnologyStockholmSweden
  2. 2.University of HelsinkiHelsinkiFinland
  3. 3.Karolinska InstitutetStockholmSweden

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