计算机科学与探索Issue(8)：963-972,10.DOI:10.3778/j.issn.1673-9418.1505013
共享子空间的多标记学习方法
Common Subspace Based Multi-Label Learning Method
摘要
Abstract
In the multi-label classification problems, different labels corresponding with the same instance may have some correlations, and the label correlation can provide some important information. Some multi-label learning methods are presented to combine the label correlation, but they are built on the original data which are high-dimensional and noisy, then they cannot obtain the satisfied results. This paper proposes a common subspace based multi-label learning method to extract high-level information from the original feature space by making use of the label information. Based on the feature mapping function, the original high-dimensional data can be effectively represented in the common low-dimensional subspace. Meanwhile, the proposed method also learns the mapping function from the label space to the common high-level feature space, then the label information of instances can be obtained with the new repre-sentation in the subspace. The experimental results on five real-world data sets show that the model can effectively improve the performance of multi-label classification.关键词
多标记学习/共享子空间/类别相关性Key words
multi-label learning/common subspace/label correlation分类
信息技术与安全科学引用本文复制引用
杨柳,邹珊,于剑,景丽萍..共享子空间的多标记学习方法[J].计算机科学与探索,2015,(8):963-972,10.基金项目
The National Natural Science Foundation of China under Grant Nos.61375062,61370129(国家自然科学基金) （国家自然科学基金）
the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20120009110006(高等学校博士学科点专项科研基金) （高等学校博士学科点专项科研基金）
the Planning Project of Science and Technology Department of Hebei Province under Grant No.13210347(河北省科技厅科技计划项目) （河北省科技厅科技计划项目）
the Project of Education Department of Hebei Province under Grant No. QN20131006(河北省教育厅资助项目) （河北省教育厅资助项目）
