基于局部强化最小二乘回归子空间分割的基因表达数据聚类  

Gene Expression Data Clustering Based on Local Strengthen Least Square Regression Subspace Segmentation

作　　者：简彩仁[1] 翁谦[2] 

机构地区：[1]厦门大学嘉庚学院,福建漳州363105 [2]福州大学数学与计算机科学学院,福建福州350116

出　　处：《三明学院学报》2016年第6期1-7,共7页Journal of Sanming University

基　　金：福建省科技创新平台建设项目(2009J1007);福建省教育厅科技项目(JK2010001)

摘　　要：利用局部约束协同表示法改进最小二乘回归子空间分割方法,提出局部强化最小二乘子空间分割方法(LSLSR)。该方法通过近邻样本的协同作用强化重构系数使得LSLSR有更好的抗噪能力,结果表明该方法有较高的聚类准确率。Subspace segmentation segments the data set into different clusters, which is broadly used in pattern recognition. Authors improve least squares regression subspace segmentation by using locality-constrained collaborative representation. Local strengthen least square regression subspace segmentation is proposed. The proposed method strengthens the reconstruct coefficient by using neighbor data samples, so that LSLSR has the ability to robust against various corruptions and occlusions. The experimental results show that the method can achieve a high clustering accuracy.

关 键 词：局部强化 最小二乘回归子空间分割 基因表达数据 聚类 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]