厦门大学学报（自然科学版）2017，Vol.56Issue(4)：567-575,9.DOI:10.6043/j.issn.0438-0479.201611021
一种基于半监督多任务学习的特征选择模型
A Feature Selection Framework Based on Semi-supervised Multi-task Learning
摘要
Abstract
Feature selection,which aims to reduce the dimension of the data and remove the redundant feature,plays an important role in improving the performance of multimedia processing.In this paper,a semi-supervised multi-task feature selection algorithm built on sharing information between multiple learning tasks has been proposed.In order to select the most discriminative features,and avoid the noise interference,we have also constructed a semi-supervised multi-task feature selection model with l2,1-norm and local information constraint.In order to verify the effectiveness of our algorithm,we apply the algorithm to the web page classification application and compare it with several state-of-the-art algorithms.Results show that the proposed algorithm is effective.关键词
特征选择/多任务学习/网页自动分类/l2,1范数Key words
feature selection/multi-task learning/web page classification/l2/1-norm分类
信息技术与安全科学引用本文复制引用
王晓栋,严菲,洪朝群..一种基于半监督多任务学习的特征选择模型[J].厦门大学学报（自然科学版）,2017,56(4):567-575,9.基金项目
国家自然科学基金(61502405) （61502405）
福建省自然科学基金(2016J01324,2017J01511) （2016J01324,2017J01511）
