该方法首先利用核主元分析对人脸图像进行特征提取，然后依据支持向量机与最近邻准则对所提取的核主元特征进行分类识别。
Firstly KPCA is used to extract the features of human face image, and then SVM combined with the nearest distance rule is used for classification, which depends on the kernel principal components.
依据同源连续性原理，通过分析高维空间中向量的方向和点的位置关系来研究模糊图像与原图像的空间关系，并且结合最近邻算法，将该算法应用于去除最近邻算法所得图像的本层模糊。
To recover blurred image but the PSF of the image was not known, using the method which according to homeomorphisms and the principle of homology continuity(PHC) in high-dimensional space geometry.
通过实验比对可知该算法效果在特征提取和分类方面均优于传统核主成分分析法以及最近邻分类器。
The experimental comparisons show that this algorithm outperforms traditional KPCA and K-Nearest Neighbor classifier on both feature extraction and classification.
通过实验比对可知该算法效果在特征提取和分类方面均优于传统核主成分分析法以及最近邻分类器。
The experimental comparisons show that this algorithm outperforms traditional KPCA and K-Nearest Neighbor classifier on both feature extraction and classification.
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