Uncorrelated kernel extension of graph embedding
具有统计不相关性的核化图嵌入算法

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.