Effect of Step-by-step Estimation Technique on Uniqueness of Solution in Nonnegative Matrix Factorization Minimizing Quasi-L1 Norm

摘要：

　　Nonnegative matrix factorization (NMF) is a linear nonnegative approximate data representation technique.NMF is often used to solve blind signal separation (BSS) problem.We had used a basic NMF algorithm named ISRA and our original algorithm named QL1-NMF to analyze the environmental electromagnetic data in extremely low frequency (ELF) band.In previous research,we found that QL1-NMF works more robust than ISRA when our data includes many outliers.However,both algorithms have a problem that their solutions are not unique.In this paper,we try to estimate signals step-by-step.We research the effect which this technique has on the uniqueness of solutions.

关键词： BSS NMF outlier initialization uniqueness

作者: Motoaki Mouri Arao Funase Andrzei Cichocki Ichi Takumi Hiroshi Yasukawa

作者单位: Faculty of Business Administration, Aichi University, Aichi, Japan Brain Science Institute, RIKEN, Saitama, Japan Dept. of Computer Science and Engineering, Nagoya Institute of Technology, Aichi, Japan Grad.School of Information Science and Technology, Aichi Prefectural University, Aichi, Japan

会议类型: 国际会议

会议名称: 2012 IEEE 11th International Conference on Signal Processing (第11届IEEE信号处理国际会议)

会议地点: 北京

会议语种:英文

页码: 157-161

在线出版日期: 2012-10-21（万方平台首次上网日期，不代表论文的发表时间）