基于RSOM聚类的局部线性嵌入算法

局部线性嵌入算法(locally linear embedding,LLE)是一种非线性降维方法.当数据量较大时,算法计算效率较低,算法运行所占用的内存空间较大.为了提高LLE算法的计算效率和减小算法运行时占用的内存空间,给出了基于RSOM(Recursive SOM)树聚类的LLE算法,通过RSOM树对数据集进行聚类,在保证输入样本依概率分布的同时显著降低算法复杂度,提高了映射效果.仿真实验表明,基于RSOM树聚类的LLE算法相对于原始的LLE算法,其算法效率有了显著提高,明显降低了算法运行所占用的内存空间,同时很好地学习了高维数据的流形结构.

LLE algorithm based on RSOM clustering

Locally linear embedding(LLE)is one of nonlinear dimensionality reduction technique.When large database is performed,the algorithm is time-consuming and huge memory space is occupied.In order to improve the efficiency of the LLE algorithm,a LLE algorithm based on RSOM tree clustering is proposed.Through clustering of RSOM tree,the computation complexity of the LLE algorithm is reduced and the probability of the database is retained.Experiments show that,compared to the original LLE algorithm,the efficiency of the RSOM tree clustering based LLE algorithm is improved remarkably and the memory space is reduced.The manifold structure of the database is also learned correctly.