Linear dimension reduction and Bayes classification with unknown population parameters

DOI10.1016/0031-3203(82)90068-1zbMath0491.62047OpenAlexW1975592322MaRDI QIDQ1167496

J. D. Tubbs, William A. Coberly, Dean M. Young

Publication date: 1982

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0031-3203(82)90068-1

zbMATH Keywords

singular value decompositiondimension reductionprojection operatorfeature selectionprobability of misclassificationBayes classification procedure

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian problems; characterization of Bayes procedures (62C10) Formal languages and automata (68Q45) Pattern recognition, speech recognition (68T10)

Related Items (9)

Dimension reduction for predictive discrimination ⋮ Effect of autocorrelated training samples on Bayes' probabilities of misclassification ⋮ Heteroscedastic linear feature extraction based on sufficiency conditions ⋮ Linear feature extraction by integrating pairwise and global discriminatory information via sequential forward floating selection and kernel QR factorization with column pivoting ⋮ Dimensionality reduction for data of unknown cluster structure ⋮ Approximate information discriminant analysis: A computationally simple heteroscedastic feature extraction technique ⋮ An efficient discriminant-based solution for small sample size problem ⋮ A formulation and comparison of two linear feature selection techniques applicable to statistical classification ⋮ Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function

Cites Work

• Distance preserving linear feature selection
• Feature selection for best mean square approximation of class densities
• Linear dimension reduction and Bayes classification
• Linear feature selection with applications
• A model for dimension reduction in pattern recognition using continuous data
• The singular value decomposition: Its computation and some applications
• Varstab: a procedure for determining homoscedastic transformations of multivariate normal populations
• Least squares, singular values and matrix approximations

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