Classification for high-dimension small-sample data sets based on Kullback-Leibler information measure

In classifying samples by Gaussian classifier, the covariance matrix estimated with a small number sample set becomes unstable, which leads to degrading the classification accuracy, in this paper, we discuss tile covariance matrix estimation problem for small number samples with high dimension setting based on Kullback-Leibler Information Measure. A new covariance matrix estimator is developed, and a fast, rough estimating regularization parameter formula is derived. Experiments are performed to investigate the classification accuracy with developed covariance matrix estimator and higher classification accuracy results are obtained.