基于聚类和改进距离的LLE方法在数据降维中的应用

局部线性嵌入算法(locally linear embedding, LLE)是解决降维的方法,针对LLE计算速度和近邻点个数K的选取,研究了该方法的扩展,提出了基于聚类和改进距离的LLE方法.基于聚类LLE方法大大缩减了计算LLE方法的时间；改进距离的LLE方法在近邻点个数取值比较小时的情况下,可得到良好的效果,而原始的LLE方法要达到相同的效果,近邻点个数K的取值通常要大很多.同时,改进距离的LLE方法可以模糊近邻点个数选取.实验结果表明，基于聚类和改进距离相结合的LLE方法相比原来的LLE方法大大提高了降维速度和扩大了参数K的选取.

Application of Dimension Reduction on Using Improved LLE Based on Clustering

Locally linear embedding (LLE) is one of the methods intended for dimension reduction. Its extension using clustering and improved LLE for dimension reduction is investigated. Firstly, using clustering can reduce time-consuming. Secondly, the improved LLE is suitable for selecting the number K of the nearest neighbors. When the number K of the nearest neighbors is small, it can obtain good results. While the original LLE algorithm obtains the same results, the number K of nearest neighbors may be much larger. Even if the number K of the nearest neighbors using the improved LLE is selected to be larger, the result is still right. So, the improved LLE is not sensitive to the selection of K. It is shown that the improved LLE based on clustering has less computing than the original LLE algorithm and enlarges the choice of parameter K by experiment.