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计算机工程 ›› 2011, Vol. 37 ›› Issue (21): 138-140. doi: 10.3969/j.issn.1000-3428.2011.21.047

• 人工智能及识别技术 • 上一篇    下一篇

基于密度刻画的降维算法

李燕燕,闫德勤,刘胜蓝   

  1. (辽宁师范大学计算机与信息技术学院,辽宁 大连 116081)
  • 收稿日期:2011-05-26 出版日期:2011-11-05 发布日期:2011-11-05
  • 作者简介:李燕燕(1986-),女,硕士研究生,主研方向:数据降维,模式识别;闫德勤,教授、博士;刘胜蓝,硕士研究生
  • 基金资助:
    辽宁省教育厅高等学校科学研究基金资助项目(20083 44);中国科学院自动化研究所复杂系统与智能科学重点实验室开放课题基金资助项目(20070101)

Dimensionality Reduction Algorithm Based on Density Portrayal

LI Yan-yan,YAN De-qin, LIU Sheng-lan   

  1. (School of Computer and Information Technology, Liaoning Normal University, Dalian 116081, China)
  • Received:2011-05-26 Online:2011-11-05 Published:2011-11-05

摘要: 针对LLE算法在数据密度变化较大时很难降维的问题,提出一种基于密度刻画的降维算法。采用cam分布寻找数据点的近邻,并在低维局部重建时对数据点加入密度信息。对手写体数字图像进行字符特征的降维,再对降维后的特征进行分类识别。实验结果表明,该方法能区分字符,具有较好的识别率,能够发现高维空间的低维嵌入流形。

关键词: 流形学习, 降维, 密度信息, 手写体识别, cam分布

Abstract: In order to improve the correctness of dimensionality reduction algorithms based on Locally Linear Embedding(LLE) caused by data density change, a novel approach based on density is proposed in this paper. It adapts cam distribute to find the data’s nearest neighbor, meanwhile, adds the data’s density information during the low dimensional local reconstruction. The proposed algorithm is used to reduce the dimensionality of input feature, and the reduced feature is classified by simple classifier. Experimental result indicates that the method can effectively improve the recognition rate of handwritten digits and can dig the manifold embedded in the high dimensional space.

Key words: manifold learning, dimensionality reduction, density information, handwriting recognition, cam distribution

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