Study on characteristic dimension and sparse factor in Non-negative Matrix Factorization algorithm

摘要：

　　Non-negative Matrix Factorization(NMF)algorithm and its variations have been successfully applied to many fields,but how to set the characteristic dimension value and the sparse factors value to improve recognition accuracy has been puzzling the researchers.Until now,it is regretful that the rigorous algorithm doesnt appear.The purpose of this paper is not to improve existing NMF algorithm to improve recognition accuracy,but emphasis on investigating how sparse factors and the characteristic dimension affect recognition accuracy,and study how to set the optimization values to characteristic dimension and sparse factors respectively to obtain the optimization recognition accuracy.A platform for face recognition is built,and some experiments are carried out with the help of the platform,finally some directional conclusions are gained.

关键词： Non-negative Matrix Factorization (NMF) characteristic dimension sparse factor

作者: Hou Mo Yang Mao-yun Qiao Shu-yun Wang Gai-ge Gao Li-qun

作者单位: School of Computer Science and Engineering,Jiangsu Normal University,Xuzhou 221116,China School of Information and Electronic Engineering,Xuzhou Institute of Technology,Xuzhou 221111,China School of Information Science & Engineering,Northeastern University,Shenyang 110819,China

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 2957-2961

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）