在统计学习理论中，尤其对于分类问题，VC维扮演着中心作用。

VC dimension plays a central role in the Statistical Learning Theory especially for classification problems.

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通过二维数据可视化和UMIST、OR L人脸识别实验，表明该方法对于分类问题具有较好的降维效果。

Experimentation based on 2-d visualization and UMIST, ORL face datasets shows that the proposed method achieves higher recognition rate.

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局部保持投影(LPP)是一种新的数据降维技术，但其本身是一种非监督学习算法，对于分类问题效果不是太好。

Locality Preserving Projections algorithm (LPP) is a new dimensionality reduction technique. But it is an unsupervised learning algorithm. It could not process classification effectively.

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