[1]杨志君,叶东毅.动态学习的非负矩阵分解算法[J].智能系统学报,2010,5(4):320-326.
YANG Zhi-jun,YE Dong-yi.A dynamic learning algorithm based on nonnegative matrix factorization[J].CAAI Transactions on Intelligent Systems,2010,5(4):320-326.
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YANG Zhi-jun,YE Dong-yi.A dynamic learning algorithm based on nonnegative matrix factorization[J].CAAI Transactions on Intelligent Systems,2010,5(4):320-326.
动态学习的非负矩阵分解算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第4期
页码:
320-326
栏目:
学术论文—人工智能基础
出版日期:
2010-08-25
- Title:
- A dynamic learning algorithm based on nonnegative matrix factorization
- 文章编号:
- 1673-4785(2010)04-0320-07
- 分类号:
- TP181
- 文献标志码:
- A
- 摘要:
- 对现有增量型非负矩阵分解算法存在的一些缺陷进行改进，给出了一个基于误差判断的增量算法有效性准则.在此基础上，利用增加样本前的非负矩阵分解结果进行增量分解初始化，提出了一种新的动态非负矩阵分解算法.在多个数据集上的实验结果表明该算法可以实现对基矩阵和编码矩阵的即时更新，且具有较低的计算复杂度，在处理动态数据集时，还可有效识别噪声点，是一个有效的动态分解算法.
- Abstract:
- To improve the performance of the incremental nonnegative matrix factorization algorithm, error estimation criteria for judging the effectiveness of the incremental algorithm was presented. Then, a new dynamic nonnegative matrix factorization algorithm was proposed whereby incremental factorization was initialized with the already factorized matrices before adding new samples. Experimental results on a number of data sets showed that the proposed algorithm is capable of instantly updating both the base matrix and the code matrix. Another benefit of the method is that the computational complexity is relatively low. The proposed algorithm can also identify noise points when dealing with dynamic data. So it is a feasible and effective dynamic factorization algorithm.
- 参考文献/References:
-
［1］LEE D D, SEUNG H S. Learning the parts of objects by nonnegative matrix factorization［J］. Nature, 1999(401): 788-791.
［2］XU Wei, LU Xin, GONG Yihong. Document clustering based on nonnegative matrix factorization［C］//Proceedings of the 26th Annual International ACM SIGIR Conference, Toronto, Canada, 2003: 267-273.
［3］GUILLAMET D, VITRIA J. Nonnegative matrix factorization for face recognition［C］//Proc Conf Topics in Artificial Intelligence. Alberta, Canada, 2002: 336-344.
［4］VIRTANEN T. Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria［J］. IEEE Transactions on Audio, Speech, and Language Processing, 2007, 15(3): 1066-1074.
［5］BUCAK S S, GUNSEL B, GURSOY O. Incremental nonnegative matrix factorization for dynamic background modeling［C］//ICEIS International Workshop on Pattern Recognition in Information Systems. Funchal, Portugal, 2007: 107-116.
［6］CAO Bin, SHEN Dou, SUN Jiantao, et al. Detect and track latent factors with online nonnegative matrix factorization［C］//The 20th International Joint Conference on Artificial Intelligence. Hyderabad, India, 2007: 2689-2694.
［7］CHEN W S, PAN B B, FANG B, et al. A novel constraint nonnegative matrix factorization criterion based incremental learning in face recognition［C］//Proceedings of International Conference on Wavelet Analysis and Pattern Recognition. Hong Kong, China, 2008: 292-297.
［8］ PETS Video Database［EB/OL］. http://ftp.pets.rdg.ac.uk/.
［9］DONOHO D， Stodden V. When does nonnegative matrix factorization give a correct decomposition into parts?［C］//Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems. Vancouver and Whistler, Canada, 2003: 101-108.
［10］LI T, DING C. The relationships among various nonnegative matrix factorization methods for clustering［C］//Sixth IEEE International Conference on Data Mining. Hong Kong, China, 2006: 362-371.
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备注/Memo
收稿日期：2009-06-08.
基金项目：国家自然科学基金资助项目（60805042）.
通信作者：叶东毅.E-mail: yiedy@fzu.edu.cn.
作者简介：
杨志君，男，1985年生，硕士研究生，主要研究方向为数据挖掘.
叶东毅，男，1964年生，教授，博士生导师，主要研究方向为计算智能、数据挖掘.曾获得1988年度国家科技进步二等奖（主要成员）、1项福建省科学技术二等奖和2项福建省科学技术三等奖.出版著作和教材6部， 发表学术论文70余篇.
更新日期/Last Update:
2010-09-20