Compressed Partial Least Squares Regression: A Supervised Method for Multi-label Data

摘要：

　　Multi-label classification allows an instance to be associated with multiple labels.Compared with other classification tasks,multi-label classification also suffers from the problem of high data dimension.However,the existing dimensionality reduction(DR)methods are not very appropriate for multi-label data.In this paper,we proposed a supervised DR method,named the compressed partial least squares regression for multi-label data(CRMD).First,CRMD aims at reducing the dimensionality of instance space and label space simultaneously,and then establishing the regression model between the two spaces for prediction.Specially,we apply 2-norm penalization on partial least squares to overcome the high dimensionality.The experimental results on six standard public datasets validate the performance of our approach.

作者: Zongjie Ma Huawen Liu Zhonglong Zheng Jianmin Zhao Xiaodan Xu

作者单位: Department of Computer Science Zhejiang Normal University Jinhua,China Department of Computer Science Zhejiang Normal University Jinhua,China;NCMIS,Academy of Mathematics

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 393-397

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）