TY - GEN
T1 - Non-negative matrix factorization using stable alternating direction method of multipliers for source separation
AU - Zhang, Shaofei
AU - Huang, Dongyan
AU - Xie, Lei
AU - Chng, Eng Siong
AU - Li, Haizhou
AU - Dong, Minghui
N1 - Publisher Copyright:
© 2015 Asia-Pacific Signal and Information Processing Association.
PY - 2016/2/19
Y1 - 2016/2/19
N2 - Nonnegative matrix factorization (NMF) is a popular method for source separation. In this paper, an alternating direction method of multipliers (ADMM) for NMF is studied, which deals with the NMF problem using the cost function of beta-divergence. Our study shows that this algorithm outperforms state-of-the-art algorithms on synthetic data sets, but it presents unstable behavior and low accuracy on real data sets. Therefore, we propose two different stable ADMM algorithms for NMF to solve this problem. They differ slightly in the multiplicative factor utilized in the update rules. One algorithm is to adapt the step size to guarantee the convergence while the other minimizes the beta-divergence with a pivot element weighting iterative method (PEWI). Experimental results demonstrate that the proposed algorithms are more stable and accurate. Particularly, PEWI based ADMM shows superior performance in the source separation task.
AB - Nonnegative matrix factorization (NMF) is a popular method for source separation. In this paper, an alternating direction method of multipliers (ADMM) for NMF is studied, which deals with the NMF problem using the cost function of beta-divergence. Our study shows that this algorithm outperforms state-of-the-art algorithms on synthetic data sets, but it presents unstable behavior and low accuracy on real data sets. Therefore, we propose two different stable ADMM algorithms for NMF to solve this problem. They differ slightly in the multiplicative factor utilized in the update rules. One algorithm is to adapt the step size to guarantee the convergence while the other minimizes the beta-divergence with a pivot element weighting iterative method (PEWI). Experimental results demonstrate that the proposed algorithms are more stable and accurate. Particularly, PEWI based ADMM shows superior performance in the source separation task.
UR - https://www.scopus.com/pages/publications/84986218146
U2 - 10.1109/APSIPA.2015.7415508
DO - 10.1109/APSIPA.2015.7415508
M3 - 会议稿件
AN - SCOPUS:84986218146
T3 - 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
SP - 222
EP - 228
BT - 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Y2 - 16 December 2015 through 19 December 2015
ER -