计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (10): 119-121.DOI: 10.3778/j.issn.1002-8331.2010.10.039

• 数据库、信号与信息处理 • 上一篇    下一篇

一种增强的局部保持投影方法

马千驰1,余国先2,钟鸿鹏1   

  1. 1.华南理工大学 软件学院,广州 510006
    2.华南理工大学 计算机科学与工程学院,广州 510006
  • 收稿日期:2009-10-19 修回日期:2009-12-21 出版日期:2010-04-01 发布日期:2010-04-01
  • 通讯作者: 马千驰

Enhanced locality preserving projection method

MA Qian-chi1,YU Guo-xian2,ZHONG Hong-peng1   

  1. 1.Department of Software Engineering,South China University of Technology,Guangzhou 510006,China
    2.Department of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China
  • Received:2009-10-19 Revised:2009-12-21 Online:2010-04-01 Published:2010-04-01
  • Contact: MA Qian-chi

摘要: 维数灾难是机器学习算法在高维数据上学习经常遇到的难题,基于局部保持的投影方法(Locality Preserving Projection,LPP),可以很好地解决维数灾难难题。然而传统LPP的相似性度量方法对噪音敏感,为此利用鲁棒路径相似的度量方法,提出一种增强的局部保持投影方法。在高维流形数据上的降维实验证实了该方法对噪声和离群点的有效性。

关键词: 维数灾难, 特征抽取, 相似性度量, 鲁棒性

Abstract: Curse of dimensionality often comes out when learning from high dimensional data.Locality Preserving Projection(LPP) can preserve the local geometry of the manifold of data and solves this problem well.However,the similarity utilized in LPP is sensitive to outliers and noise,thus an Enhanced Locality Preserving Projection(ELPP) method is proposed with the robust path based similarity.Experiments on high dimensional manifold data prove the effectiveness of this method.

Key words: curse of dimensionality, feature extraction, similarity metric, robustness

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