On α-divergence based nonnegative matrix factorization for clustering cancer gene expression data

Authors:
Weixiang Liu;Kehong Yuan;Datian Ye
Affiliations:
Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China;Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China;Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China
Venue:
Artificial Intelligence in Medicine
Year:
2008

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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 @a-divergence for NMF. However, it is an open problem to choose an optimal @a. Methods and materials: In this paper, we tested such NMF variant with different @a values on clustering cancer gene expression data for optimal @a selection experimentally with 11 datasets. Results and conclusion: Our experimental results show that @a=1 and 2 are two special optimal cases for real applications.