摘要：

结合稀疏约束与邻近交替线性化(PALM),提出稀疏非负矩阵分解算法(SNMF_PALM)。将非凸的平滑剪切绝对偏差函数作为稀疏正则项,获得逼近L0范数的最佳凸松弛,并利用PALM算法对非凸问题进行求解,得到SNMF_PALM算法的局部稳定最优解。在人脸数据库上将SNMF_PALM算法与SNMF、NMF算法进行实验对比,结果表明SNMF_PALM算法具有更好的聚类性能。

关键词: 非负矩阵分解, 稀疏, 平滑剪切绝对偏差函数, 邻近交替线性化, 非凸问题, 聚类

Abstract:

This paper combinessparsity constraint and Proximal Alternating Linearized Minimization(PALM),proposes a Sparse Non-negative Matrix Factorization(SNMF) algorithm,called SNMF_PALM.The non-convex Smoothly Clipped Absolute Deviation(SCAD) function is used as the sparse regularization term to obtain the optimal convex relaxation approximating L0 norm.The PALM algorithm is used to solve the non-convex problem,and the local optimal solution of SNMF_PALM algorithm is obtained.Experimental results of SNMF_PALM,SNMF and improved NMF algorithms in face database show that SNMF_PALM algorithm has better clustering performance.

Key words: Non-negative Matrix Factorization(NMF), sparse, Smoothly Clipped Absolute Deviation(SCAD) function, Proximal Alternating Linearized Minimization(PALM), non-convex problem, clustering

中图分类号: 

• TP301.6