【摘要】 近邻保持嵌入算法(NPE)是一种保持数据流形上局部结构的子空间学习算法,它是对局部线性嵌入的线性逼近.然而当数据为图像时,图像被拉直为向量后的维数通常非常高,而样本点有限,由于矩阵的奇异性,NPE不能直接运用.我们将NPE推广到二维情形,提出二维近邻保持嵌入算法(2D-NPE).2D-NPE直接在二维图像矩阵上提取图像特征,而不是把图像拉直成一维向量后再提取特征.通过在手写数字字符图像库和Yale人脸图像库上的实验,验证算法的有效性.更多还原

【Abstract】 Neighbourhood preserving embedding(NPE) is a subspace learning algorithm,which aims at preserving the local neighbourhood structure on the data manifold,and it is a linear approximation to Locally Linear Embedding(LLE).When image data are concerned,the dimensionality of vectorized image data is usually high.NPE can not be implemented due to singularity of matrix.NPE is extended to 2 dimensional senses,2DNPE,which directly extracts image feature from 2D image matrices rather than from 1D vectors as NPE does.The proposed algorithm is evaluated on Yale face database and Binary Alpha digits database.更多还原