A Novel Fast Non-negative Matrix Factorization Algorithm and Its Application in Text Clustering

被引：0
|
作者：
Li, Fang [1 ]
Zhu, Qunxiong [1 ]
机构：
[1] Beijing Inst Chem Technol, Sch Informat Sci & Technol, Beijing 100029, Peoples R China
来源：
ADVANCES IN SWARM INTELLIGENCE, PT 2, PROCEEDINGS | 2010年 / 6146卷
关键词：
Term-Document Matrix; Non-negative Matrix Factorization (NMF); Text Clustering;
D O I：
10.1063/1.3493090
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In non-negative matrix factorization, it is difficult to find the optimal non-negative factor matrix in each iterative update. However, with the help of transformation matrix, it is able to derive the optimal non-negative factor matrix for the transformed cost function. Transformation matrix based nonnegative matrix factorization method is proposed and analyzed. It shows that this new method, with comparable complexity as the priori schemes, is efficient in enhancing nonnegative matrix factorization and achieves better performance in NMF based text clustering.
引用
收藏
页码：375 / 382
页数：8
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