【摘要】 <正>1引言随着科学技术的发展,人类社会进入了大数据时代,高维数据普遍出现在医学、生物学和信息技术等领域.直接处理这些高维数据存在很大的困难,经典方法难以适用.为了发现高维数据相关特征,必须有效处理高维数据,其中关键在于降低数据维数.矩阵分解技术是解决这个问题的有效途径之一.更多还原

【Abstract】 Nonnegative Matrix Factorization(NMF) is an effective matrix decomposition technique which has been widely used in many fields including image processing,text mining,pattern analysis and so on.Based on the projection of function gradient,we propose a novel projected gradient method for solving nonnegative linear least squares problem,and analyze its convergence properties.Applying the developed projected gradient method to the NMF problem,we present a new alternating projected gradient method for computing NMF.Numerical results show that the proposed method is superior to both the multiplicative update algorithm and the projected gradient method for the NMF.更多还原