Comparative analysis of statistical pattern recognition methods in high dimensional settings

Pattern Recognition

Volumn 27, Issue 8, 1994, Pages 1065-1077

  Aeberhard, Stefan ^a   Coomans, Danny ^a   de Vel, Olivier ^a  

^a JAMES COOK UNIVERSITY   (Australia)

Author keywords

Classifier evaluation; Dimensionality; Discriminant analysis; High dimensionality; Reduction; Simulation

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; DATA STRUCTURES; ERRORS; STATISTICAL METHODS; THREE DIMENSIONAL;

CLASSIFIER EVALUATION; DISCRIMINANT ANALYSIS; HIGH DIMENSIONALITY; TWO DIMENSIONAL;

PATTERN RECOGNITION;

EID: 0028483559     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/0031-3203(94)90145-7     Document Type: Article

References (15)

1. Average classification accuracy over collections of Gaussian problems-common covariance matrix case
2. Effects of sample size in classifier design
3. Optimal discriminant plane for a small number of samples and design method of classifier on the plane
4. Regularized Discriminant Analysis
5. Improvements to the classification performance of RDA
6. On extensions to Fisher's linear discriminant function
7. An optimal transformation for discriminant and principal component analysis
8. Covariance pooling and stabilization for classification
9. The performance of statistical pattern recognition methods in high dimensional settings
10. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS