An enhanced initialization method for non-negative matrix factorization

Gong, Liyun and K. Nandi, Asoke (2013) An enhanced initialization method for non-negative matrix factorization. In: Machine Learning for Signal Processing (MLSP), 2013 IEEE International Workshop on, 22 - 25 Sept 2013, Chilworth, Southampton.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Non-negative matrix factorization (NMF) is a dimensionality reduction tool, and has been applied to many areas such as bioinformatics, face image classification, etc. However, it often converges to some local optima because of its random initial NMF factors (W and H matrices). To solve this problem, some researchers have paid much attention to the NMF initialization problem. In this paper, we first apply the k-means clustering to initialize the factor W, and then we calculate the initial factor H using four different initialization methods (three standard and one new). The experiments were carried out on the eight real datasets and the results showed that the proposed method (EIn-NMF) achieved less error and faster convergence compared with both random initialization based NMF and the three standard methods for k-means based NMF.

Keywords:K-means clustering, Nonnegative matrix factorization
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:28872
Deposited On:03 Oct 2017 08:33

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