Comparison of Discrimination Methods for High Dimensional Data
From MaRDI portal
Publication:5439655
DOI10.14490/jjss.37.123zbMath1138.62361OpenAlexW1992581342MaRDI QIDQ5439655
Tatsuya Kubokawa, Muni S. Srivastava
Publication date: 11 February 2008
Published in: JOURNAL OF THE JAPAN STATISTICAL SOCIETY (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.14490/jjss.37.123
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Theory of matrix inversion and generalized inverses (15A09) Empirical decision procedures; empirical Bayes procedures (62C12)
Related Items (16)
Covariance structure approximation via gLasso in high-dimensional supervised classification ⋮ Comparison of linear shrinkage estimators of a large covariance matrix in normal and non-normal distributions ⋮ A statistical approach to set classification by feature selection with applications to classification of histopathology images ⋮ Improved Stein-type shrinkage estimators for the high-dimensional multivariate normal covariance matrix ⋮ Discriminant analysis in small and large dimensions ⋮ Unnamed Item ⋮ A model selection criterion for discriminant analysis of high-dimensional data with fewer observations ⋮ Integrated use of statistical-based approaches and computational intelligence techniques for tumors classification using microarray ⋮ Accuracy of regularized D-rule for binary classification ⋮ High-dimensional mean estimation via \(\ell_1\) penalized normal likelihood ⋮ A comparison of regularization methods applied to the linear discriminant function with high-dimensional microarray data ⋮ Asymptotic properties of the EPMC for modified linear discriminant analysis when sample size and dimension are both large ⋮ Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data ⋮ Weighted linear programming discriminant analysis for high‐dimensional binary classification ⋮ Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations ⋮ Two-Stage Procedures for High-Dimensional Data
This page was built for publication: Comparison of Discrimination Methods for High Dimensional Data