【摘 要】
：
矩阵模式主分量分析(MatPCA)作为有效的特征提取方法能同时处理矩阵表式的模式和向量表式的模式。但与主分量分析(PCA)方法一样,MatPCA没有使用样本的类别信息,因此所提取的特征不能提供足够的判别信息,进而影响随后的分类性能。为有效利用样本的类别信息,在MatPCA基础上提出了一种新的特征提取方法——模糊的类内MatPCA(F-WMatPCA)。F-WMatPCA利用模糊K最近邻(FKNN)
【出 处】
：
Transactions of Nanjing University of Aeronautics & Astronau
论文部分内容阅读
矩阵模式主分量分析(MatPCA)作为有效的特征提取方法能同时处理矩阵表式的模式和向量表式的模式。但与主分量分析(PCA)方法一样,MatPCA没有使用样本的类别信息,因此所提取的特征不能提供足够的判别信息,进而影响随后的分类性能。为有效利用样本的类别信息,在MatPCA基础上提出了一种新的特征提取方法——模糊的类内MatPCA(F-WMatPCA)。F-WMatPCA利用模糊K最近邻(FKNN)求解训练样本的模糊隶属度并在模糊的类内执行MatPCA。由于F-WMatPCA使用更多的类别信息,因此能
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