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Improved Locally Linear Embedding Through New Distance Computing.
- Source :
- Advances in Neural Networks - ISNN 2006; 2006, p1326-1333, 8p
- Publication Year :
- 2006
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540344391
- Database :
- Supplemental Index
- Journal :
- Advances in Neural Networks - ISNN 2006
- Publication Type :
- Book
- Accession number :
- 32883812
- Full Text :
- https://doi.org/10.1007/11759966_197