The principal component analysis can estimate the intrinsic dimensionality of the hyper-plane.
主成分分析能有效估计这一几何体的本征维数。
The accurate intrinsic dimension estimation also profit to select an appropriate neighborhood size in data processing for avoiding dimensionality curse.
并且，在数据处理过程中，准确的本征维数估计对选取合适的邻域大小有很大的帮助，可以避免“维数灾难”。
Manifold learning attempts to obtain the intrinsic structure of non-linearly distributed data, which can be used in non-linear dimensionality reduction(NLDR).
流形学习旨在获得非线性分布数据的内在结构，可以用于非线性降维。
Manifold learning attempts to obtain the intrinsic structure of non-linearly distributed data, which can be used in non-linear dimensionality reduction(NLDR).
流形学习旨在获得非线性分布数据的内在结构，可以用于非线性降维。
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