计算机科学 ›› 2015, Vol. 42 ›› Issue (2): 256-259.doi: 10.11896/j.issn.1002-137X.2015.02.053

• 人工智能 • 上一篇    下一篇

一种邻域竞争线性嵌入的降维方法

李燕燕,闫德勤   

  1. 河北建筑工程学院 张家口075024,辽宁师范大学 大连116081
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(61105085)资助

Dimensionality Reduction Algorithm Based on Neighborhood Rival Linear Embedding

LI Yan-yan and YAN De-qin   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对局部线性嵌入算法处理稀疏数据失效的问题,提出一种基于邻域竞争线性嵌入的降维方法。利用数据的统计信息动态确定局部线性化范围,并采用cam分布寻找数据点的近邻,避免了近邻选取方向的缺失。在数据集稀疏的情况下,通过对数据点近邻做局部结构的提取,该算法能够很好地把握数据的局部信息和整体信息。为了验证算法的有效性,将该算法应用于手工流形降维和对Corel数据库进行图像检索等,结果表明该算法不仅有较好的降维效果,而且具有很好的实用价值。

关键词: 线性化,流形学习,局部线性嵌入,稀疏,降维

Abstract: In order to improve the correctness of locally linear embedding caused by sparse data,a novel dimensionality reduction algorithm based on neighborhood rival linear embedding was proposed in this paper.According to the statistical information,it determines local linear dynamic range,adopts the cam distribution to find neighbors of data points,and avoids the lack of the direction of neighbor selection.In the case of sparse data sets,the algorithm can effectively obtain local and global information of data.The experiment to test the improved algorithm obtains a good effort of reducing dimension.The experimental results on the image retrieval using the Corel database show the efficiency of the algorithm.

Key words: Linearization,Manifold learning,Locally linear embedding,Sparse,Dimensionality reduction

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