Clustering by non-negative matrix factorization with independent principal component initialization

Gong, Liyun and K. Nandi, Asoke (2013) Clustering by non-negative matrix factorization with independent principal component initialization. In: 21st European Signal Processing Conference (EUSIPCO 2013), 9 - 13 Sept 2013, Palais de Congres, Marrakech.

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


Non negative matrix factorization (NMF) is a dimensionality reduction and clustering method, and has been applied to many areas such as bioinformatics, face images classification, and so on. Based on the traditional NMF, researchers recently have put forward several new algorithms on the initialization area to improve its performance. In this paper, we explore the clustering performance of the NMF algorithm, with emphasis on the initialization problem. We propose an initialization method based on independent principal component analysis (IPCA) for NMF. The experiments were carried out on the four real datasets and the results showed that the IPCA-based initialization of NMF gets better clustering of the datasets compared with both random and PCA-based initializations.

Keywords:Non-negative matrix factorization; Principal component analysis; Independent component analysis; Independent principal component analysis
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:28871
Deposited On:03 Oct 2017 08:27

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