摘要
子空间分割方法是一种重要的机器学习方法,现有的子空间分割方法一般在原样本空间上进行研究.文中借鉴现有的降维方法将投影技术与最小二乘回归子空间分割方法结合,提出投影子空间分割的基因表达数据聚类方法.该方法通过交替优化求解投影矩阵和重构矩阵,同时实现降维和聚类.在6个基因表达数据集上的实验表明文中方法的有效性.
Subspace segmentation method is an important method for machine learning. The existing researches on subspace segmentation method are generally on the original sample space. Advanced by existing dimensional reduction methods, a gene expression data clustering method based on projection subspace segmentation is proposed by joining projection method and least square regression based subspace segmentation. Projection matrix and remodeling matrix is got by using alternate optimization, and dimension reduction and cluster is ＇realized simultaneously. The experimental results on six gene expression datasets illustrate the validity of the proposed method.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2015年第8期728-734,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.71273053)
福建省自然科学基金项目(No.2014J01009)资助
关键词
基因表达数据
聚类
最小二乘回归
子空间分割
投影
Gene Expression Data, Clustering, Least Square Regression, SubspaceSegmentation, Projection
