Weixiang Liu1, Kehong Yuan, Datian Ye. 1. Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China. victorwxliu@yahoo.com
Abstract
OBJECTIVE: Nonnegative matrix factorization (NMF) has been proven to be a powerful clustering method. Recently Cichocki and coauthors have proposed a family of new algorithms based on the alpha-divergence for NMF. However, it is an open problem to choose an optimal alpha. METHODS AND MATERIALS: In this paper, we tested such NMF variant with different alpha values on clustering cancer gene expression data for optimal alpha selection experimentally with 11 datasets. RESULTS AND CONCLUSION: Our experimental results show that alpha=1 and 2 are two special optimal cases for real applications.
OBJECTIVE: Nonnegative matrix factorization (NMF) has been proven to be a powerful clustering method. Recently Cichocki and coauthors have proposed a family of new algorithms based on the alpha-divergence for NMF. However, it is an open problem to choose an optimal alpha. METHODS AND MATERIALS: In this paper, we tested such NMF variant with different alpha values on clustering cancer gene expression data for optimal alpha selection experimentally with 11 datasets. RESULTS AND CONCLUSION: Our experimental results show that alpha=1 and 2 are two special optimal cases for real applications.