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


CONCLUSIONS
Our experimental results show that alpha=1 and 2 are two special optimal cases for real applications.
Liu
Weixiang
W
Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China. victorwxliu@yahoo.com
Yuan
Kehong
K
Ye
Datian
D
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D052138:Genes, Neoplasm; D006801:Humans; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results