基于模糊聚类的改进LLE算法

局部线性嵌入法(Locally Linear Embedding,LLE)是一种基于流形学习的非线性降维方法。针对LLE近邻点个数选取、样本点分布以及计算速度的问题,提出基于模糊聚类的改进LLE算法。算法根据聚类中心含有大量的信息这一特点,基于模糊聚类原理,采用改进的样本点距离计算方法,定义了近似重构系数,提高了LLE计算速度,改进了模糊近邻点个数的选取。实验结果表明,改进的算法有效地降低了近邻点个数对算法的影响,具有更好的降维效果和更高的计算速度。

An Improved LLE Dimensionality Reduction Algorithm Based on FCM

Locally Linear Embedding（LLE） is one of the non-linear dimensionality reduction methods which are based on manifold learning. Focused on the existing problem of the selection of the neighborhood and the distribution of sample points, also the time-consuming, an improved LLE algorithm for dimension reduction was proposed. Based on fuzzy clustering theory and distance calculation method, by making use of the characteristic of cluster center including massive information, this paper defined the approximately reconstructing coefficient. The experimental results show that the improved LLE can reduce the influence of the number of neighbors efficiently and obtain good results, also it can reduce time-consuming.