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基于局部线性嵌入法的流形学习 被引量:1

Study Based on Locally Linear Embedding
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摘要 本文介绍了一种非线性降维方法—局部线性嵌入法,并通过实例与PCA对比,论证了LLE在处理非线性高维数据中的优越性。 In this paper, a nonlinear dimensionality reduction methods - Locally Linear Embedding is introduced. Comparing with PCA, LLE performs better for nonlinear highdimensional data.
作者 黄移军
出处 《数学理论与应用》 2009年第4期38-42,共5页 Mathematical Theory and Applications
关键词 降维 局部线性嵌入法 主成分法 可视化 Dimensionality reduction Locally linear embedding Principal component analysis Visualization
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参考文献10

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同被引文献12

  • 1贺玲,吴玲达,蔡益朝.数据挖掘中的聚类算法综述[J].计算机应用研究,2007,24(1):10-13. 被引量:225
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  • 5De la Torte F, Kanade T. Discriminative cluster analysis [ C ] //Proc of International Conference on Machine Learning. New York : ACM ,2006:241 -248.
  • 6Ding C, Li T. Adaptive dimension reduction using discriminant analysis and k - means clustering [ C ]//Proc of International Conference on Machine Learning. New York : ACM, 2007 : 521 -528.
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  • 8I Frank A, Asuncion A. UCI machine learning repository [ D ]. Irvine, CA : University of California ,2010.
  • 9于洪涛,段军义,杜照丰.一种基于聚类技术的个性化信息检索方法[J].计算机工程与应用,2008,44(8):187-188. 被引量:12
  • 10周爱武,于亚飞.K-Means聚类算法的研究[J].计算机技术与发展,2011,21(2):62-65. 被引量:134

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