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Linear dimension reduction in classification: adaptive procedure for optimum results

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  • Karsten Luebke
  • Claus Weihs

Abstract

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  • Karsten Luebke & Claus Weihs, 2011. "Linear dimension reduction in classification: adaptive procedure for optimum results," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(3), pages 201-213, October.
    • Handle: RePEc:spr:advdac:v:5:y:2011:i:3:p:201-213
    DOI: 10.1007/s11634-011-0091-x
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    References listed on IDEAS

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    1. Michael C. Röhl & Claus Weihs & Winfried Theis, 2002. "Direct Minimization of Error Rates in Multivariate Classification," Computational Statistics, Springer, vol. 17(1), pages 29-46, March.
    2. Weihs, Claus & Luebke, Karsten, 2005. "Improving Feature Extraction by Replacing the Fisher Criterion by an Upper Error Bound," Technical Reports 2005,19, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    1. Luebke, Karsten & Weihs, Claus, 2004. "Generation of prediction optimal projection on latent factors by a stochastic search algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 297-310, September.
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