Improved locally linear embedding through new distance computing

Authors:
Heyong Wang;Jie Zheng;Zhengan Yao;Lei Li
Affiliations:
Software Research Institute, Sun Yat-sen University, Guangzhou, China;College of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, China;College of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, China;Software Research Institute, Sun Yat-sen University, Guangzhou, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 2
Cited 3

Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Self-Organizing Maps

Self-Organizing Maps

LDR-LLE: LLE with Low-Dimensional Neighborhood Representation

ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data

Neurocomputing
Locally linear embedding: a survey

Artificial Intelligence Review

Quantified Score

Hi-index	0.01

Visualization

Abstract

Locally linear embedding (LLE) is one of the methods intended for dimensionality reduction, which relates to the number K of nearest-neighbors points to be initially chosen. So, in this paper, we want that the parameter K has little influence on the dimension reduction, that is to say, the parameter K can be widely chosen while not influence the effect of dimension reduction. Therefore, we propose a method of improved LLE, which uses new distance computing for weight of K nearest-neighbors points in LLE. Thus, even when the number K is little, the improved LLE can get good results of dimension reduction, while the traditional LLE needs a larger number of K to get the same results. When the number K of the nearest neighbors gets larger, test in this paper has proved that the improved LLE can still get correct results.