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Locally linear embedding algorithm based on adaptiveneighborhood selection(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年06期
Page:
748-
Research Field:
Publishing date:

Info

Title:
Locally linear embedding algorithm based on adaptiveneighborhood selection
Author(s):
Zhang Zhiyou1Zhou Jiayan2Shao Haijian3Bao Anping1
1.School of CQC New Energy Technology,Nanjing College of Information Technology,Nanjing 210023,China; 2.Department of Electrical Engineering,Jiangsu Province Nanjing Engineering Vocational College,Nanjing 211135,China; 3.School of Computer Science and E
Keywords:
adaptive neighborhood selection locally linear embedding sparse matrices dimension reduction manifold algorithm
PACS:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2017.41.06.013
Abstract:
An improved locally linear embedding(LLE)algorithm based on local neighborhood-dependent weights and sparse matrices is proposed to improve the computation efficiency of dimensionality reduction for high-dimensional data.The correlation dimension estimation method is used to estimate the intrinsic information of the dimensionality reduction in high-dimensional data and the upper bound of the uniform manifold.Five classical datasets,including Swiss,Broken swiss,Helix,Twinpeaks and Intersect,are used to assess the algorithm.The results show that,compared with that of local linear embedding algorithm,the calculation speed of this algorithm on the five datasets is improved by 27.60%,27.51%,27.18%,28.31% and 45.28% respectively.

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Last Update: 2017-12-31