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LR-SVM plus : Learning Using Privileged Information with Noisy Labels
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AI
DOI：10.1109/TMM.2021.3116417.png)
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摘要
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The paradigm of Learning Using Privileged Information (LUPI) always assumes that labels are annotated precisely. However, in practice, this assumption may be violated, as the labels may be heavily noisy, which inevitably degenerates the performance of learning algorithms in the LUPI paradigm. To handle the side effect of noisy labels, we propose a novel Label Noise Robust SVM+ (LR-SVM+) algorithm. Specifically, as the privileged information contains rich information of the latent labels, we first utilize it to infer underlying clean labels. Then we use the inference to modify the noisy labels. Comprehensive experiments demonstrate the necessity of studying label noise robust SVM+ and the effectiveness of the proposed method.
Keyword:
Noise measurement
Support vector machines
Training
Optimization
Robustness
Linear programming
Task analysis
SVM plus
privileged information
noisy labels
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4.6K
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