title: On alpha-divergence based nonnegative matrix factorization for clustering cancer gene expression data.
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
  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.
authors.affiliation: Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China. victorwxliu@yahoo.com
authors.forename: Weixiang
authors.initials: W
authors.lastname: Liu
authors.forename: Kehong
authors.initials: K
authors.lastname: Yuan
authors.forename: Datian
authors.initials: D
authors.lastname: Ye
mesh_terms: 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