提出统计不相关的核化图嵌入算法，为求解各种统计不相关的核化降维算法提供了一种统一方法。与已有核化降维算法相比，新的特征提取方法降低甚至消除了最佳鉴别矢量间的统计相关性，提高了识别率。通过在ORL，YALE和FERET人脸库上的实验结果表明，提出的具有统计不相关的核化图嵌入算法在识别率方面好于已有的核算法。另外，揭示了统计不相关的核化图嵌入与已有的核化图嵌入的内在关系。

An uncorrelated kernel extension of graph embedding which provides a unified method for computing all kinds of uncorrelated kernel dimensionality reduction algorithms is proposed. Compared with kernel dimensionality reduction methods, the proposed method is better in terms of reducing or eliminating the statistical correlation between features and improving the recognition rate. The experimental results on ORL, YALE and FERET face databases show that the proposed uncorrelated kernel extension of graph embedding method is better than other methods in terms of recognition rate. Besides, the relation between uncorrelated kernel extension of graph embedding and kernel extension of graph embedding is revealed.