计算机工程与应用 ›› 2012, Vol. 48 ›› Issue (7): 135-138.

• 数据库、信号与信息处理 • 上一篇    下一篇

基于数据点松紧度的局部线性嵌入算法

斯庆巴拉1,郎德琴2   

  1. 1.北华航天工业学院 计算机科学与工程系,河北 廊坊 065000
    2.北京一轻高级技术学校 计算机系,北京 100068
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2012-03-01 发布日期:2012-03-01

Local linear embedding algorithm based on tightness of data points

SIQING Bala1, LANG Deqin2   

  1. 1.Department of Computer Science and Engineering, North China Institute of Aerospace Engineering, Langfang, Hebei 065000, China
    2.Department of Computer, Beijing One Light School of Senior Technology, Beijing 100068, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2012-03-01 Published:2012-03-01

摘要: 局部线性嵌入算法(LLE)是流形学习中非线性数据降维的重要方法之一。考虑数据点分布大多呈现不均匀性,LLE对近邻点的选取方式将会导致大量的信息丢失。根据其不足,提出一种基于数据点松紧度的局部线性嵌入改进算法——tLLE算法,针对数据点分布不均匀的数据集,tLLE算法能有效地进行维数约简,且具有比LLE更好的降维效果。在人造数据和现实数据上的嵌入以及分类识别结果表明了tLLE算法的有效性。

Abstract: Locally Linear Embedding(LLE) algorithm is one of the nonlinear data dimensionality reduction approaches based on manifold learning. Considering the distribution of data points mostly present the heterogeneity, there will result in large amounts of information loss when LLE selects neighboring points. This paper proposes a novel locally linear embedding algorithm based on tightness of data points, named tLLE, which can reduce dimensionality effectively for the datasets that present the non-uniform distribution. And, it has better effects of dimensionality reduction than LLE. The embedding and classification results on synthetic and real data show that tLLE is very effective.