基于α-散度的非负矩阵分解用于聚类癌症基因表达数据
On alpha-divergence based nonnegative matrix factorization for clustering cancer gene expression data.
作者信息
Liu Weixiang, Yuan Kehong, Ye Datian
机构信息
Research Center of Biomedical Engineering, Life Science Division, Graduate school at Shenzhen, Tsinghua University, Shenzhen 518055, China.
出版信息
Artif Intell Med. 2008 Sep;44(1):1-5. doi: 10.1016/j.artmed.2008.05.001. Epub 2008 Jul 3.
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.
目的
非负矩阵分解（NMF）已被证明是一种强大的聚类方法。最近，齐乔茨基及其合著者提出了一系列基于α-散度的NMF新算法。然而，选择最优的α仍然是一个悬而未决的问题。
方法和材料
在本文中，我们在11个数据集上通过实验测试了这种具有不同α值的NMF变体，用于在聚类癌症基因表达数据时选择最优α。
结果与结论
我们的实验结果表明，α = 1和2是实际应用中的两个特殊最优情况。
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