Semi-Parametric Polynomial Inverse Regression for Dimension Reduction and Its Application in Microarray Data

摘要：

As the development of the gene microarray technology, the contradiction between number of genes and sample size has become more apparent, high-dimension independent variables also challenge the traditional nonparametric methods. A new method for dimension reduction, Semi-parametric Polynomial Inverse Regression ( SPPIR ), based on sliced inverse regression is proposed. By simulation, we demonstrate how SPPIR can reduce the dimension of the input variables effectively. In the end, we conduct SPPIR and discriminant analysis for a tumor gene microarray data. By comparing with other methods, the effectiveness of dimension reduction methods can be seen.

关键词： Dimension Reduction Gene Inverse Regression Principal Component Analysis

作者: Zhang Guofen Zhang Yan

作者单位: Institute of Statistics Zhejiang University Hangzhou,China 310027

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）