Name
Abstract | ||
|---|---|---|
A new covariance matrix estimator useful for designing classifiers with limited training data is developed. In experiments, this estimator achieved higher classification accuracy than the sample covariance matrix and common covariance matrix estimates. In about half of the experiments, it achieved higher accuracy than regularized discriminant analysis, but required much less computation. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/34.506799 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
limited training data,common covariance matrix estimate,higher classification accuracy,new covariance matrix estimator,covariance matrix estimation,sample covariance matrix,regularized discriminant analysis,higher accuracy,classification,discriminant analysis,analysis of variance,impedance,estimation,remote sensing,maximum likelihood estimation,euclidean distance,parameter estimation,training data,labeling,covariance matrix,high dimensional data,cross validation | Covariance function,Estimation of covariance matrices,Pattern recognition,Computer science,Rational quadratic covariance function,Covariance intersection,Artificial intelligence,CMA-ES,Covariance matrix,Matérn covariance function,Scatter matrix | Journal |
Volume | Issue | ISSN |
18 | 7 | 0162-8828 |
Citations | PageRank | References |
136 | 20.44 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph P. Hoffbeck | 1 | 137 | 21.18 |
| David A. Landgrebe | 2 | 807 | 125.38 |