Dual-scale correlation analysis for robust multi-label classification
Dual-scale correlation analysis for robust multi-label classification
复制标题
用于稳健多标签分类的双尺度相关分析
DOI：
10.1007/s10489-022-03299-5
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发表时间：
2022-03
影响因子：
5.3
通讯作者：
Lei Wang
中科院分区：
文献类型：
--
作者：
Kaixiang Wang;Ming Yang;Wanqi Yang;Lei Wang
Noise in label space is a major challenge in the multi-label classification problem, as the noise (including false-negative noise and false-positive noise) can affect the distribution of the label space, which causes serious interference to the performance of the learned model. Existing methods have initially solved the situation where false-negative noise and false-positive noise appear separately, but classification when the two kinds of noise appear at the same time is still a challenging problem that has not yet been solved. This paper proposed a novel method named Dual-Scale Correlation Analysis for Robust Multi-label Classification (DCAMC) to deal with the above challenge, which can effectively deal with the simultaneous occurrence of these two kinds of noise. Our proposed method is based on the dual scale correlation analysis of samples and can mainly be divided into two parts, anti-noise module and classification module. In the anti-noise module, we define novel ‘leader-labels’ and ‘rare-labels’ based on the manifold assumption under fine-grained and coarse-grained data division respectively. The novel anti-noise module can solve the problem of false-negative noise and false-positive noise simultaneously without interfering with each other; In the classification module, we use the training datasets after the anti-noise process to train the multi-label classifiers. Coarse-grained data division for classification training guarantees the generalization performance of the model while fine-grained data division ensures effective label correlations mining. The two effective modules based on dual-scale data division improve the overall classification performance. Our method has been tested on the existing datasets, and the experiments demonstrate that our method has an improvement over existing methods.
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全局扩展，局部收缩：缺失标签的判别式多标签学习
影响因子：
8
作者：
Zhongchen Ma;Songcan Chen
通讯作者：
Songcan Chen
DOI：
--
发表时间：
2012-12
期刊：
--
影响因子：
--
作者：
Yao-Nan Chen;Hsuan-Tien Lin
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Yao-Nan Chen;Hsuan-Tien Lin
DOI：
--
发表时间：
2017-07
期刊：
ArXiv
影响因子：
--
作者：
Chih-Kuan Yeh;Wei-Chieh Wu;Wei-Jen Ko;Y. Wang
通讯作者：
Chih-Kuan Yeh;Wei-Chieh Wu;Wei-Jen Ko;Y. Wang
DOI：
10.24963/ijcai.2018/398
发表时间：
2018-07
期刊：
--
影响因子：
--
作者：
X. Wu;Min-Ling Zhang
通讯作者：
X. Wu;Min-Ling Zhang
DOI：
10.1109/iccv.2009.5459266
发表时间：
2009-09
期刊：
2009 IEEE 12th International Conference on Computer Vision
影响因子：
--
作者：
M. Guillaumin;Thomas Mensink;J. Verbeek;C. Schmid
通讯作者：
M. Guillaumin;Thomas Mensink;J. Verbeek;C. Schmid