research-article

Learning with Noisy Partial Labels by Simultaneously Leveraging Global and Local Consistencies

Authors:

Jihong OuyangAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 725 - 734

https://doi.org/10.1145/3340531.3411885

Published: 19 October 2020 Publication History

Abstract

In real-world scenarios, the data are widespread that are annotated with a set of candidate labels but a single ground-truth label per-instance. The learning paradigm with such data, formally referred to as Partial Label (PL) learning, has recently drawn much attention. The traditional PL methods estimate the confidences being the ground-truth label of candidate labels with various regularizations and constraints, however, they only consider the local information, resulting in potentially less accurate estimations as well as worse classification performance. To alleviate this problem, we propose a novel PL method, namely PArtial label learNing by simultaneously leveraging GlObal and Local consIsteNcies (Pangolin). Specifically, we design a global consistency regularization term to pull instances associated with similar labeling confidences together by minimizing the distances between instances and label prototypes, and a local consistency term to push instances marked with no same candidate labels away by maximizing their distances. We further propose a nonlinear kernel extension of Pangolin, and employ the Taylor approximation trick for efficient optimization. Empirical results demonstrate that Pangolin significantly outperforms the existing PL baseline methods.

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Cited By

Li CDai YFeng LLi XWang BOuyang JSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Positive and unlabeled learning with controlled probability boundary fenceProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693176(27641-27652)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693176
Lü SKang MLi XLarson K(2024)Alleviating imbalanced pseudo-label distributionProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/516(4669-4677)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/516
Ma JJiang JBao WZhang H(2024)Partial label learning via identifying outlier featuresKnowledge-Based Systems10.1016/j.knosys.2024.112278301(112278)Online publication date: Oct-2024
https://doi.org/10.1016/j.knosys.2024.112278
Show More Cited By

Index Terms

Learning with Noisy Partial Labels by Simultaneously Leveraging Global and Local Consistencies
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Published: 19 October 2020

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Key R&D Projects of Science and Technology Department of Jilin Province, China
National Natural Science Foundation of China (NSFC)

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CIKM '20

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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Li CDai YFeng LLi XWang BOuyang JSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Positive and unlabeled learning with controlled probability boundary fenceProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693176(27641-27652)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693176
Lü SKang MLi XLarson K(2024)Alleviating imbalanced pseudo-label distributionProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/516(4669-4677)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/516
Ma JJiang JBao WZhang H(2024)Partial label learning via identifying outlier featuresKnowledge-Based Systems10.1016/j.knosys.2024.112278301(112278)Online publication date: Oct-2024
https://doi.org/10.1016/j.knosys.2024.112278
Wang WZhang MZhang ARangwala H(2022)Partial Label Learning with Discrimination AugmentationProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539363(1920-1928)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539363
Wang YGuan YWang BLi X(2022)Learning with partial multi-labeled data by leveraging low-rank constraint and decompositionApplied Intelligence10.1007/s10489-022-03989-053:7(8133-8145)Online publication date: 28-Jul-2022
https://dl.acm.org/doi/10.1007/s10489-022-03989-0
Li CLi XOuyang JWang YDemartini GZuccon GCulpepper JHuang ZTong H(2021)Detecting the Fake Candidate InstancesProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482251(903-912)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482251

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