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β散度的非负矩阵分解在基因聚类中的应用

发表时间:2015-02-28  浏览量:2659  下载量:1665
全部作者: 崔建,何光辉
作者单位: 重庆大学数学与统计学院
摘 要: 针对传统的非负矩阵分解(non-negative matrix factorization,NMF)算法在基因表达数据聚类应用中的低效性,研究采用一种基于β散度的NMF(β-NMF)算法,该方法中β的选择是一个开放性问题。实验中β取不同值分别对基因表达数据进行分解,再通过K均值聚类。分析结果并与传统的基于梯度下降的NMF和Kullback Leibler(KL)散度的NMF实验做对比,得出当β取值为0.5时,该算法对基因数据表达谱具有较好的聚类效果。
关 键 词: 模式识别;β散度;非负矩阵分解;基因表达数据;聚类;梯度下降
Title: Application of β-divergence non-negative matrix factorization in gene clustering
Author: CUI Jian, HE Guanghui
Organization: College of Mathematics and Statistics, Chongqing University
Abstract: Traditionally non-negative matrix factorization (NMF) algorithm clustering for the application of gene expression data is inefficient. In this paper, we used NMF algorithm based on β divergence (β-NMF) to avoid some insufficiency of classical NMF algorithm. It was an open problem to choose the best optimal value of β. In our experiments, we took different value of β for gene expression data decomposition, and then clustered gene expression data by K-means algorithm. By analyzing and comparing experimental results with traditional non-negative matrix decomposition based on gradient descent and Kullback Leibler (KL) divergence decomposition, we concluded that when β was 0.5, the algorithm had an better clustering effect.
Key words: pattern recognition; β divergence; non-negative matrix factorization; gene expression data; clustering;gradient descent
发表期数: 2015年2月第4期
引用格式: 崔建,何光辉. β散度的非负矩阵分解在基因聚类中的应用[J]. 中国科技论文在线精品论文,2015,8(4):325-330.
 
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