计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 473-477.

• 大数据与数据挖掘 • 上一篇    下一篇

在线学习非负矩阵分解

何孝文, 胡一飞, 王海平, 陈默   

  1. 广东工业大学自动化学院 广州510006
  • 出版日期:2019-06-14 发布日期:2019-07-02
  • 通讯作者: 何孝文(1994-),男,硕士生,主要研究方向为在线非负矩阵分解及其应用,E-mail:786551691@qq.com
  • 作者简介:胡一飞(1993-),男,硕士生,主要研究方向为稀疏非负矩阵分解;王海平(1993-),男,硕士生,主要研究方向为心肺音分离的方法及应用;陈 默(1995-),男,硕士生,主要研究方向为非负矩阵分解在心电信号中的应用。

Online Learning Nonnegative Matrix Factorization

HE Xiao-wen, HU Yi-fei, WANG Hai-ping, CHEN Mo   

  1. School of Automation,Guangdong University of Technology,Guangzhou 510006,China
  • Online:2019-06-14 Published:2019-07-02

摘要: 文中提出了一种新的在线形式的非负矩阵分解,即在线学习非负矩阵分解(OLNMF)。OLNMF算法采用了增量形式的非光滑模型,并采用“选择遗忘法”控制新样品和旧样品的权重,提高了算法的计算效率,减少了计算复杂度。OLNMF算法能处理大型的实时更新的数据集,并得到稀疏度更高的基矩阵。实验结果表明,在多个人脸数据集中,相对于INMF,ONMFO,Lp-INMF,OLNMF具有更好的稀疏性;在EEG数据集中,基于OLNMF的SVM分类方法能得到更好的分类准确率。

关键词: 非负矩阵分解, 特征提取, 稀疏约束, 在线学习

Abstract: This paper proposed a new nonnegative matrix factorization of online form,namely online learning nonnegative matrix factorization(OLNMF).The OLNMF algorithm uses incremental forms of non-smooth model,and adopts “anmesic average method” to control the weight of new and old samples,improving the computational efficiency and reducing the computational complex.OLNMF algorithm can deal with large real-time update data sets,and extract more sparse base matrix.Compared with INMF,ONMFO,Lp-INMF,experiments on face databases show that the proposed method achieves better sparsity,andSVM classification method base on OLNMF achieves better classification accuracy on EEG database.

Key words: Feature extraction, Nonnegative matrix factorization, Online learning, Sparseness constraints

中图分类号: 

  • TP391
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