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

WANG Wen-jun

doi:10.3969/j.issn.0372-2112.2016.04.017

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

更新时间：2025-07-16

- New Method of Dimensionality Reduction for Gene Expression Data Based on Sparse Class Preserving Projection
- Acta Electronica Sinica Vol. 44, Issue 4, Pages: 873-877(2016)
- 作者机构：
  
  1. 西安电子科技大学计算机学院,陕西,西安,710071
  2. 西安电子科技大学计算机学院,陕西,西安,710071
- 作者简介：
- 基金信息：
  
  Fundamental Research Funds for the Central Universities (No.JB140310)
- DOI：10.3969/j.issn.0372-2112.2016.04.017
  CLC： TP391
- Published Online：25 April 2016，
  
  Published：2016
- 稿件说明：
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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.
DOI：

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. DOI： 10.3969/j.issn.0372-2112.2016.04.017.

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.

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.

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