计算机工程与应用2017，Vol.53Issue(5)：81-84,96,5.DOI:10.3778/j.issn.1002-8331.1507-0300
应用谱回归和图正则最小二乘回归的数据降维
Dimension reduction method based on spectral regression and graph regularization least square regression
摘要
Abstract
Data dimension reduction is significant to research high-dimensional data. Sparse concept coding receives widespread attention, but the sparse representation coefficients fail to maintain the essential structure of the data. In re-sponse to this discovery, a method based on spectral regression and graph regularization least square regression for data di-mension reduction is proposed. The experiments on two image data sets and two gene expression data sets show the pro-posed method is better than the unimproved sparse concept coding.关键词
谱回归/图正则最小二乘回归/降维/聚类Key words
spectral regression/graph regularization least square regression/dimension reduction/clustering分类
信息技术与安全科学引用本文复制引用
翁谦,毛政元,林嘉雯,简彩仁..应用谱回归和图正则最小二乘回归的数据降维[J].计算机工程与应用,2017,53(5):81-84,96,5.基金项目
福建省科技创新平台建设项目(No.2009J1007) （No.2009J1007）
福建省教育厅科技项目(No.JK2010001). （No.JK2010001）
