基于散度差准则的隐空间特征抽取方法

本文提出了一种新的非线性特征抽取方法——基于散度差准则的隐空间特征抽取方法。该方法的主要思想就是首先利用一核函数将原始输入空间非线性变换到隐空间，然后，在该隐空间中，利用类间离散度与类内离散度之差作为鉴别准则进行特征抽取。与现有的核特征抽取方法不同，该方法不需要核函数满足Mercer定理，从而增加了核函数的选择范围。更为重要的是，由于采用了散度差作为鉴别准则，从根本上避免了传统的Fisher线性鉴别分析所遇到的小样本问题。在ORL人脸数据库和AR标准人脸库上的试验结果验证了本文方法的有效性。

Feature Extraction Method Based on Scatter Difference Criterion in Hidden Space

In this paper, a novel feature extraction method based on scatter difference criterion in hidden space is developed. Its main idea is that the original input space is first mapped into a hidden space through a prespecified kernel function, in which space the feature extraction is conducted using the difference of between-class scatter and within-class scatter as the discriminant criterion. Different from the existing kernel feature extraction methods, the kernel function used in the proposed one is not required to satisfy Mercer's theorem so that they can be chosen from a wide range. It is more important that due to adoption of the scatter difference as the discriminant criterion for feature extraction, the proposed method essentially avoids the small size samples problem usually occured in the traditional Fisher linear discriminant analysis. Finally, extensive experiments are performed on ORL face database and AR face database. The experimental results indicate that the proposed method outperforms the traditional scatter difference discriminant analysis in recognition performance.