A Method of Feature Selection and Classification Based on Divergence
基于分离度的图象特征提取与识别方法

The separability of two pattern classes can be measured by the divergence for two Gaussian distributions. Because the divergence is invariant under the linear transformation we can extract "good" features for separating two patterns via Karhunen-Loeve transformation. It is shown that the divergence is only dependent on two of n eigenvalues. One property of a "good" dichotomy is that each feature should be effective for classification. Thus, a criterion function is proposed. This algorithm, which is called as sample exchange algorithm, is convergent and it is a reasonable unsupervised clustering method for classification.