Partial Label Learning with Unlabeled Data

Wang, Qian-Wei; Li, Yufeng; Zhou, Zhi‐Hua

doi:10.24963/ijcai.2019/521

Cited by 30 publications

(19 citation statements)

References 16 publications

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“…Step size for w supervised multi-label learning (Wei et al 2018), semisupervised weak label learning (Dong, Li, and Zhou 2018) and semi-supervised partial label learning (Wang, Li, and Zhou 2019). It is worth noting that our paper is different from these works as we focus on the mix of severe label noise and biased label distribution.…”

Section: Related Workmentioning

confidence: 99%

IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience

Guo

Kuang²,

Liu³

et al. 2020

AAAI

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Weakly supervised learning aims at coping with scarce labeled data. Previous weakly supervised studies typically assume that there is only one kind of weak supervision in data. In many applications, however, raw data usually contains more than one kind of weak supervision at the same time. For example, in user experience enhancement from Didi, one of the largest online ride-sharing platforms, the ride comment data contains severe label noise (due to the subjective factors of passengers) and severe label distribution bias (due to the sampling bias). We call such a problem as ‘compound weakly supervised learning’. In this paper, we propose the CWSL method to address this problem based on Didi ride-sharing comment data. Specifically, an instance reweighting strategy is employed to cope with severe label noise in comment data, where the weights for harmful noisy instances are small. Robust criteria like AUC rather than accuracy and the validation performance are optimized for the correction of biased data label. Alternating optimization and stochastic gradient methods accelerate the optimization on large-scale data. Experiments on Didi ride-sharing comment data clearly validate the effectiveness. We hope this work may shed some light on applying weakly supervised learning to complex real situations.

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Section: Related Workmentioning

confidence: 99%

IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience

Guo

Kuang²,

Liu³

et al. 2020

AAAI

Self Cite

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“…Although manually labeled instances can make up for the lack of labeled instance to a certain extent, this process is time-consuming and laborious. Then, the semi-supervised learning (SSL; Zhou et al, 2003 , 2014 ; Zhu et al, 2003 ; Chapelle et al, 2006 ; Zhu, 2008 ; Zhu and Goldberg, 2009 ; Gao et al, 2010 ; Zhou and Li, 2010 ; Zhao and Zhou, 2018 ; Tao et al, 2019 ; Wang Q.-W. et al, 2019 ; Wang T.-Z. et al, 2019 ; Zhang et al, 2019c ) technique was proposed, which learns a model from a small amount of labeled instances and a large amount of unlabeled instances and solves the problem of insufficient labeled instance (i.e., poor generalization of the model obtained by supervised learning and inaccurate models obtained by unsupervised learning).…”

Section: Introductionmentioning

confidence: 99%

“…While the GSSL inference models have been employed on EEG datasets due to its effectiveness and intuitiveness, limited effort has been made on improving its performance by the clustering assumption. One of the most common assumptions is the clustering hypothesis: "Similar instances should share the same class label" (Chapelle et al, 2006;Zhu and Goldberg, 2009;Xue et al, 2011;Zhou et al, 2014;Wang Q.-W. et al, 2019;Wang T.-Z. et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition

Dan

Tao

et al. 2021

Front. Neurosci.

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The purpose of the latest brain computer interface is to perform accurate emotion recognition through the customization of their recognizers to each subject. In the field of machine learning, graph-based semi-supervised learning (GSSL) has attracted more and more attention due to its intuitive and good learning performance for emotion recognition. However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in the same scenario. To address the problem, in this paper, we invent a Possibilistic Clustering-Promoting semi-supervised learning method for EEG-based Emotion Recognition. Specifically, it constrains each instance to have the same label membership value with its local weighted mean to improve the reliability of the recognition method. In addition, a regularization term about fuzzy entropy is introduced into the objective function, and the generalization ability of membership function is enhanced by increasing the amount of sample discrimination information, which improves the robustness of the method to noise and the outlier. A large number of experimental results on the three real datasets (i.e., DEAP, SEED, and SEED-IV) show that the proposed method improves the reliability and robustness of the EEG-based emotion recognition.

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“…In recent years, the fault classification method has been very successful for bearing based on an assumption, which is that candidate label sets are provided for all training examples [6]. Based on this assumption, lots of effort are taken on traditional intelligent fault diagnosis approaches.…”

Section: Introductionmentioning

confidence: 99%

Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

et al. 2021

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A new tensor transfer approach is proposed for rotating machinery intelligent fault diagnosis with semisupervised partial label learning in this paper. Firstly, the vibration signals are constructed as a three-way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor-aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.

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Partial Label Learning with Unlabeled Data

Cited by 30 publications

References 16 publications

IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience

IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience

Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition

Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

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