文章摘要
引用本文:张海武,陈晓云.基于判别信息的近邻保持嵌入降维方法[J].福州大学学报(自然科学版),2015,43(4):466~470
基于判别信息的近邻保持嵌入降维方法
Dimensionality reduction of neighborhood preserving embedding based on discrimination information
  
DOI:10.7631/issn.1000-2243.2015.04.0466
中文关键词: 降维  近邻保持嵌入  判别信息  局部结构
英文关键词: dimensionality reduction  neighborhood preserving embedding  discrimination information  local structure
基金项目:
作者单位
张海武 福州大学数学与计算机科学学院福建 福州 350116 
陈晓云 福州大学数学与计算机科学学院福建 福州 350116 
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中文摘要:
      针对传统近邻保持嵌入算法(NPE)侧重保持样本的局部结构,而没有考虑样本类别信息的不足,提出判别局部近邻保持嵌入算法DLNPE. 该算法利用样本点的局部结构构造新定义下的类内类间散布矩阵,并以此作为判别信息引入目标函数. 在6个真实数据上进行实验,证明了所提算法的有效性.
英文摘要:
      Because traditional neighborhood preserving embedding (NPE) focuses on keeping a sample of local structure without taking category information into account,the discriminant local neighborhood preserving embedding algorithm is proposed. The algorithm use the local structure of samples points to construct the new definition within-class and between-class scatter matrix,which are introduced into the objective function as the discrimination information. The experimental results on six real data show that the algorithm is effective.
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