On alpha-divergence based nonnegative matrix factorization for clustering cancer gene expression data.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 18602254.
- Also identified by DOI 10.1016/j.artmed.2008.05.001.
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Abstract
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. 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. Our experimental results show that alpha=1 and 2 are two special optimal cases for real applications.
Medical subject headings
- Algorithms
- Artificial Intelligence
- Cluster Analysis
- Gene Expression Profiling
- Genes, Neoplasm