In this paper, We obtain the total genus polynomials for two new classes of 4-regular graphs by using the joint tree model of a graph embedding introduced by Yanpei Liu.
本文利用刘彦佩提出的嵌入的联树模型，得出了两类新的四正则图的完全亏格多项式，并推导出已有结果的两类图的完全亏格多项式。
An uncorrelated kernel extension of graph embedding which provides a unified method for computing all kinds of uncorrelated kernel dimensionality reduction algorithms is proposed.
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。
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.
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。
Besides, the relation between uncorrelated kernel extension of graph embedding and kernel extension of graph embedding is revealed.
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。
Besides, the relation between uncorrelated kernel extension of graph embedding and kernel extension of graph embedding is revealed.
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。
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