Joint Label-Specific Features and Correlation Information for Multi-Label Learning  

作　　者：Xiu-Yi Jia Sai-Sai Zhu Wei-Wei Li 

机构地区：[1]School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China [2]College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China

出　　处：《Journal of Computer Science & Technology》2020年第2期247-258,共12页计算机科学技术学报（英文版）

基　　金：Supported by:This work was partially supported by the National Natural Science Foundation of China under Grant Nos.61773208 and 61906090;the Natural Science Foundation of Jiangsu Province of China under Grant Nos.BK20191287 and BK20170809.

摘　　要：Multi-label learning deals with the problem where each instance is associated with a set of class labels.In multilabel learning,different labels may have their own inherent characteristics for distinguishing each other,and the correlation information has shown promising strength in improving multi-label learning.In this study,we propose a novel multilabel learning method by simultaneously taking into account both the learning of label-specific features and the correlation information during the learning process.Firstly,we learn a sparse weight parameter vector for each label based on the linear regression model,and the label-specific features can be extracted according to the corresponding weight parameters.Secondly,we constrain label correlations directly on the output of labels,not on the corresponding parameter vectors which conflicts with the label-specific feature learning.Specifically,for any two related labels,their corresponding models should have similar outputs rather than similar parameter vectors.Thirdly,we also exploit the sample correlations through sparse reconstruction.The experimental results on 12 benchmark datasets show that the proposed method performs better than the existing methods.The proposed method ranks in the 1st place at 66.7%case and achieves optimal average rank in terms of all evaluation measures.

关 键 词：MULTI-LABEL learning label-specific FEATURE SPARSE reconstruction LABEL CORRELATION SAMPLE CORRELATION 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]