南京理工大学学报（自然科学版）Issue(4)：518-525,8.
基于结构和约束保持的半监督特征选择
Semi-supervised feature selection based on structure and constraints preserving
摘要
Abstract
To overcome the deficiency of most existing feature selection methods which fairly respect both the geometrical structure and the supervision information, a novel approach called semi-supervised feature selection based on structure and constraints preserving is proposed. In this method,both the pairwise constraints and the local and nonlocal structure are taken into account,and a new feature selection criterion,i. e. structure and constraints preserving( SCP) score is defined. The SCP score exploites abundant unlabeled data points to learn the geometrical structure of the data space,and uses a few pairwise constraints to discover the margins of different classes. Those features that can preserve the geometrical structure and pairwise constraints information are selected. Experimental results from several datasets show that the proposed method achieves better performance than the feature ranking selection methods.关键词
特征选择/半监督学习/成对约束/结构和约束保持/特征排序/空间结构/先验知识Key words
feature selection/semi-supervised learning/pairwise constrains/structure and constraints preserving/feature ranking/geometrical structure/supervision information分类
信息技术与安全科学引用本文复制引用
潘俊,王瑞琴,孔繁胜..基于结构和约束保持的半监督特征选择[J].南京理工大学学报（自然科学版）,2014,(4):518-525,8.基金项目
浙江省科技计划项目(2012C33086) （2012C33086）
浙江省自然科学基金(LQ12F02008) （LQ12F02008）
