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A Geometric Structure Preserving Non-negative Matrix Factorization for Data Representation
Authors
唐延东
李冰锋
韩志
Publication date
1 January 2017
Publisher
Abstract
作为一种线性降维方法,非负矩阵分解(NMF)算法在多个场合均有应用;但NMF算法只能在欧氏空间上进行语义分解,当输入数据是嵌入在高维空间的低维流形时,NMF会引入较大的分解误差。为解决此问题,本文提出了一种基于几何结构保持的非负矩阵分解算法(SPNMF)。在SPNMF算法中,我们将局部近邻样本点间的相似性关系的保持和远距离非近邻样本点间的互斥性关系的保持引入到NMF框架;并把非负矩阵分解的求解问题转化为数值优化问题,然后用交替优化的方法对SPNMF算法进行了求解。相对于NMF,SPNMF算法拥有更多的数据分布的先验知识,因此SPNMF算法可以获得一种更好低维数据表达方式.在人脸数据库上的试验结果表明,相对于NMF及其它的改进算法,SPNMF算法具有更高的聚类精度
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Shenyang Institute of Automation,Chinese Academy Of Sciences
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Last time updated on 12/02/2018