Department of Computer Science
Analysis of Gene Expression Data Using RPEM Algorithm in Normal Mixture Model with Dynamic Adjustment of Learning Rate
Microarray technology is a useful tool for monitoring the expression levels of thousands of genes simultaneously. Recently, mixture modeling has been used to extract expression signatures from gene expression profiles. In general, two separate steps are utilized to estimate the number of classes and model parameters, respectively. However, such a method is often time-consuming and leads to suboptimal solutions. In this paper, we therefore apply a one-step approach, namely Rival Penalized Expectation-Maximization (RPEM) algorithm, to analyze the gene expression data. The RPEM algorithm is capable of estimating the parameters of normal mixture model, while determining the number of classes automatically at the same time. Furthermore, we speed up the learning procedure of RPEM by proposing a new mechanism to adjust the learning rate dynamically. The numerical results on real gene expression data demonstrate that our proposed method is indeed effective and efficient. © 2010 World Scientific Publishing Company.
Clustering, dynamic adjustment of learning rate, gene expression, normal mixture model, rival penalized EM algorithm
Source Publication Title
International Journal of Pattern Recognition and Artificial Intelligence
World Scientific Publishing
Link to Publisher's Edition
Zhao, Xing-Ming, Yiu-Ming Cheung, and De-Shuang Huang. "Analysis of Gene Expression Data Using RPEM Algorithm in Normal Mixture Model with Dynamic Adjustment of Learning Rate." International Journal of Pattern Recognition and Artificial Intelligence 24.4 (2010): 651-666.