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Updated: Jul 3, 2026
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
Published on: October 11, 2019
On alpha-divergence based nonnegative matrix factorization for clustering cancer gene expression data
Weixiang Liu1, Kehong Yuan, Datian Ye
1Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China. victorwxliu@yahoo.com
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.
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