摘要
本文研究了基于核技巧的L_(2,1)范数非负矩阵分解在图像聚类中的问题.利用基于核的稀疏鲁棒非负矩阵分解方法,获得了算法良好的稀疏性和鲁棒性,提高了聚类性能,该方法也可以推广到文本聚类的应用.
The problem of norm non-negative matrix factorization with L2,1 is studied based on kernel technique in image clustering.By kernel-based sparse robust non-negative matrix factorization method,the sparseness and robustness of the algorithm are obtained,and the clustering performance is improved.This method can also be extended to the application of text clustering.
作者
余江兰
李向利
董晓亮
YU Jiang-lan;LI Xiang-li;DONG Xiao-liang(School of Mathematics and Computing Science,Guangxi Key Laboratory of Cryptography and Information Security,Guangxi Key Laboratory of Automatic Detecting Technology and Instruments,Guilin University of Electronic Technology,Guilin 541004,China;School of Mathematics and Information Science,North Minzu Univeisity,Yinchuan 750021,China)
出处
《数学杂志》
2019年第3期440-454,共15页
Journal of Mathematics
基金
国家自然科学基金(11601012
71561008)
广西自然科学基金(2018GXNSFAA138169)
广西密码学与信息安全重点实验室研究课题(GCIS201708)
广西自动检测技术与仪器重点实验室基金(YQ16112
YQ18112)
宁夏自然科学基金(NZ17103)
桂林电子科技大学研究生优秀学位论文培育项目资助(16YJPYSS22)
关键词
非负矩阵分解
核技巧
L2
1范数
稀疏性
鲁棒性
non-negative matrix factorization
kernel trick
L2,1 norm
sparsity
robustness
