Kernel Locality Preserving Projection (KLPP) is a nonlinear dimension reduction method, which combines the kernel trick with the manifold learning method effectively.
2、核局部保留投影(kernel locality preserving projection, KLPP)是一种非线性的维数约简方法,它将核方法和流形学习思想有效地结合起来。
参考来源 - 基于流形的特征抽取及人脸识别研究The appearance of kernel trick which can derive non-linear feature improves the develepment of face recognition, and feature extraction technology such as kernel principal component analysis and kernel fisher linear discriminant analysis get more attention.
最近,核技术的发展进一步促进了这两种传统的特征抽取方法的发展,出现了核主成分分析和核鉴别分分析这两种非线性的特征抽取方法,可以解决原始的样本在线性空间可能不可分的问题,基于核的特征提取方法也得到了迅速的发展。
参考来源 - 基于子空间分析的特征抽取及人脸识别技术研究·2,447,543篇论文数据，部分数据来源于NoteExpress
以上来源于: WordNet
This algorithm is a combination of kernel trick with the covering algorithm, and is used to extract the support vectors in feature space.
该算法将核技巧与覆盖算法相结合，并在特征空间中抽取支持向量。
By introducing the kernel trick to the canonical correlation analysis(CCA), a feature fusion method based on kernel CCA(KCCA) is established and is then used to capture the associated feat.
该方法首先采集侧面视角人脸图像，然后将核方法引入到典型相关分析（CCA）中，提出基于核CCA的特征融合方法，并应用其提取人耳人脸的关联特征进行个体的分类识别。
The main idea is to approximate the classical local linear embedding (LLE) by introducing a linear transformation matrix and then find the solution in a very high dimensional space by kernel trick.
其主要思想是通过引入线性变换矩阵来近似经典的局部线性嵌入(LLE)，然后通过核方法的技巧在高维空间里求解。
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