Abstract: Locally Linear Embedding(LLE) algorithm is sensitive for the number of nearest neighbors, and fails on sparse source data. In order to solve the problem, this paper proposes a new LLE algorithm based on improved distance and united optimization. It introduces a distance measure in Conformal-IsoMap into LLE, and units the two optimization equation of the original algorithm. Experimental results on SwissRoll curve sampling and MINST character database validate the effectiveness of the algorithm.

Key words: manifold learning, Local Linear Embedding(LLE), denotation coordinate, embedded coordinate

摘要： 局部线性嵌套(LLE)算法对近邻个数较敏感，无法处理稀疏数据源。针对该问题提出一种基于改进距离和联合优化的LLE算法。将Conformal-IsoMap中度量数据间距离的方法引入到LLE，并对原算法的2个优化过程进行联合优化。在SwissRoll曲线采样数据和MINST手写数字字符数据库上的实验结果验证了该算法的有效性。

关键词: 流形学习, 局部线性嵌套, 表示坐标, 嵌入坐标

CLC Number: 

• TP391