计算机科学 ›› 2015, Vol. 42 ›› Issue (5): 309-314.doi: 10.11896/j.issn.1002-137X.2015.05.063
张其文,庄新磊
ZHANG Qi-wen and ZHUANG Xin-lei
摘要： 基于流形学习理论的近邻保持嵌入算法(Neighborhood Preserving Embedding,NPE)能够发现数据集中隐含的内蕴结构,但当训练样本不足时,无法准确发现数据的内在流形结构,从而影响算法的识别效果。针对这一问题,对NPE算法进行改进,提出了监督协同近邻保持投影算法(Supervised Collaborative Neighborhood Preserving Projection,SCNPP)。该算法在类别信息的指导下构建近邻图,使同类样本间的几何关系得到保持,利用协同表示弥补NPE因样本不足造成的表示误差,以一个有效保持样本近邻关系、准确发现数据内在流形结构的权值矩阵计算投影矩阵,提高分类效果。在FERET、AR和Extended Yale B人脸数据集上的实验验证了该算法的有效性。
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