A Joint Embedded Multi-label Classification Algorithm

被引：0
|
作者：
Liu H.-T. [1 ,2 ]
Leng X.-Y. [1 ,2 ]
Wang L.-L. [1 ,2 ]
Zhao P. [1 ,2 ]
机构：
[1] Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei
[2] School of Computer Science and Technology, Anhui University, Hefei
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Denoising auto-encoder; Label embedding; Matrix factorization; Multi-label classification;
D O I：
10.16383/j.aas.c180087
中图分类号：
学科分类号：
摘要：
Some existing classification algorithms become infeasible anymore, because most multi-label data contains high-dimensional features or label information. To solve this problem, a joint embedded multi-label learning classification algorithm named Deep AE-MF is proposed in this paper, which is based on denoising auto-encoder and matrix factorization. The algorithm includes two parts: the feature embedding part uses denoising auto-encoder to obtain the nonlinear representation of feature space learning, and the label embedding part directly learns the potential representation and decoding matrix of the corresponding label space using matrix factorization. In order to get an effective classification model, Deep AE-MF combines the two phases of feature embedding and label embedding to learn a potential space for model prediction. To further improve the performance of the model, the negative correlation between tags is exploited in Deep AE-MF. Experiments on different datasets show the effectiveness and robustness of the proposed Deep AE-MF method. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
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页码：1969 / 1982
页数：13
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