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

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.

Supplementary Material

MP4 File (3340531.3411885.mp4)
The presentation video of Learning with Noisy Partial Labels by Simultaneously Leveraging Global and Local Consistencies, which is accepted in CIKM2020.

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  • (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
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  1. Learning with Noisy Partial Labels by Simultaneously Leveraging Global and Local Consistencies

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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
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 19 October 2020

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      Author Tags

      1. instance dissimilarity
      2. kernel extension
      3. label prototype
      4. partial noisy label
      5. single-label classification

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

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      Cited By

      View all
      • (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
      • (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
      • (2024)Partial label learning via identifying outlier featuresKnowledge-Based Systems10.1016/j.knosys.2024.112278301(112278)Online publication date: Oct-2024
      • (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
      • (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
      • (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

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