·2,447,543篇论文数据，部分数据来源于NoteExpress
根据中心流形定理，高维映射与被降维的简化映射的局部分岔行为是等价的。
According to center manifold theory, high dimensional map and simplified map are equivalent near by bifurcation point.
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).
LPLE算法解决了传统LLE算法在源数据稀疏情况下的不能有效进行降维的问题，这也是其他传统的流形学习算法没有解决的。
LPLE is better than LLE in that it gives the global coordinates of the sparse data and this isn't be resolved by the other conventional algorithm.
应用推荐