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基于监督学习的稀疏编码及在数据表示中的应用

DOI: 10.13195/j.kzyjc.2013.0274, PP. 1115-1119

Keywords: 矩阵分解,鉴别分析,稀疏编码,数据表示,拟合

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Abstract:

针对稀疏编码在数据表示时没有利用样本类别信息的问题,提出一种基于监督学习的稀疏编码算法,并应用于数据表示.首先利用样本的类别信息构建图,直接提取样本的鉴别结构信息;然后利用基向量拟合鉴别结构特性向量,进而在基向量中嵌入样本的鉴别信息;最后对样本逐个进行稀疏表示.在COIL20和PIE图像库的实验结果表明,相比几种无监督矩阵分解算法,所提出的算法更利于样本的表示和分类.

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