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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.
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。
An uncorrelated kernel extension of graph embedding which provides a unified method for computing all kinds of uncorrelated kernel dimensionality reduction algorithms is proposed.
另外，基于(核)最大间距准则，本文提出了一组具有统计不相关性的最优(核)鉴别矢量集的计算方法。
Besides, based on the (kernel) maximum margin criterion, new algorithms of statistically uncorrelated optimal (kernel) discriminant vectors for feature extraction is presented in this paper.
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