期刊文献+

基于密度信息的改进降维方法

Improvement method of dimensionality reduction through mining density information
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摘要 扩散映射(diffusion maps)是一种基于流形学习的非线性降维方法。为了提高降维的效果,根据近邻点的选取对diffusion maps的降维效果影响,利用数据近邻点分布的不同,挖掘该数据点局部的密度信息,能够更好地保持数据的流形结构。利用样本点聚类后的类别信息构造密度信息指数,提出了一种改进的diffusion maps算法,有效地保持了高维数据中的流形结构,所提的新算法在多种实验中得到了证实。 Diffusion maps is based on manifold learning nonlinear dimensionality reduction method. The effective of the diffusion maps dimension reduction is affected by the distribution of the neighboring points, mining local density information , it can better maintain the structure of the data. Using sample points after clustering to construct density coefficient, this paper proposed an improved method of diffusion maps algorithm, effective to keep the high dimension data of the manifold structure, and confirmed the proposed method in the experiments.
出处 《计算机应用研究》 CSCD 北大核心 2013年第11期3292-3294,共3页 Application Research of Computers
基金 国家自然科学基金资助项目(61105085) 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) 辽宁省教育厅高等学校科学研究基金资助项目(2008344)
关键词 流形学习 降维 聚类 扩散映射 manifold learning dimensionality reduction cluster diffusion maps
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参考文献16

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