基于稀疏类别保留投影的基因表达数据降维方法

doi:10.3969/j.issn.0372-2112.2016.04.017

电子学报 ›› 2016, Vol. 44 ›› Issue (4): 873-877.DOI: 10.3969/j.issn.0372-2112.2016.04.017

基于稀疏类别保留投影的基因表达数据降维方法

王文俊

西安电子科技大学计算机学院, 陕西西安 710071

收稿日期:2014-07-23 修回日期:2014-12-22 出版日期:2016-04-25
- 作者简介:
- 王文俊 女,1980年8月出生,山西平遥人,副教授、硕士生导师.2003年、2006年和2011年在西安电子科技大学分别获得工学学士、工学硕士和工学博士学位.2006年至今在西安电子科技大学计算机学院从事教学科研工作,主要研究方向为模式识别、生物信息处理等. E-mail:xidianwwj219@163.com
- 基金资助:
- 中央高校基本科研业务费专项资金 (No.JB140310)

New Method of Dimensionality Reduction for Gene Expression Data Based on Sparse Class Preserving Projection

WANG Wen-jun

School of Computer Science and Engineering, Xidian University, Xi'an, Shaanxi 710071, China

Received:2014-07-23 Revised:2014-12-22 Online:2016-04-25 Published:2016-04-25
- Supported by:
- Fundamental Research Funds for the Central Universities (No.JB140310)

摘要/Abstract

摘要：

针对基因表达数据高维小样本特性所带来的维数灾难问题,结合回归和类别保留投影方法,提出一种新的基因表达数据降维方法,叫稀疏类别保留投影.相比类别保留投影,能有效避免类别保留投影在基因表达数据降维上存在的矩阵奇异和过拟合问题.通过对真实基因表达数据进行数据可视化和分类识别,验证了方法的有效性.

关键词: 基因表达数据, 高维小样本, 类别保留投影, 回归

Abstract:

To solve the problem of the curse of dimensionality of gene expression data due to the characteristic of high dimension low sample size,a new method of dimensionality reduction for gene expression data,called sparse class preserving projection (SCPP) is proposed,by combining regression and class preserving projection(CPP).Compared to CPP,SCPP can avoid the problems of matrix singularity and over-fitting.Experiments are performed on gene expression data for visualization and sample classification,and the results confirm the effectiveness of the method.

Key words: gene expression data, high dimension and low sample size, class preserving projection, regression

中图分类号:

TP391

王文俊. 基于稀疏类别保留投影的基因表达数据降维方法[J]. 电子学报, 2016, 44(4): 873-877.

WANG Wen-jun . New Method of Dimensionality Reduction for Gene Expression Data Based on Sparse Class Preserving Projection[J]. Acta Electronica Sinica, 2016, 44(4): 873-877.

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基于稀疏类别保留投影的基因表达数据降维方法

New Method of Dimensionality Reduction for Gene Expression Data Based on Sparse Class Preserving Projection

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