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Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism

Published: 20 August 2020 Publication History

Abstract

Partial Multi-Label learning (PML) learns from the ambiguous data where each instance is associated with a candidate label set, where only a part is correct. The key to solve such problem is to disambiguate the candidate label sets and identify the correct assignments between instances and their ground-truth labels. In this paper, we interpret such assignments as instance-to-label matchings, and formulate the task of PML as a matching selection problem. To model such problem, we propose a novel grapH mAtching based partial muLti-label lEarning (HALE) framework, where Graph Matching scheme is incorporated owing to its good performance of exploiting the instance and label relationship. Meanwhile, since conventional one-to-one graph matching algorithm does not satisfy the constraint of PML problem that multiple instances may correspond to multiple labels, we extend the traditional probabilistic graph matching algorithm from one-to-one constraint to many-to-many constraint, and make the proposed framework to accommodate to the PML problem. Moreover, to improve the performance of predictive model, both the minimum error reconstruction and k-nearest-neighbor weight voting scheme are employed to assign more accurate labels for unseen instances. Extensive experiments on various data sets demonstrate the superiority of our proposed method.

Supplementary Material

MP4 File (3394486.3403053.mp4)
An introduction of our paper titled "Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism"

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cover image ACM Conferences
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
August 2020
3664 pages
ISBN:9781450379984
DOI:10.1145/3394486
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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Publication History

Published: 20 August 2020

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Author Tags

  1. 'instance-to-label' matching
  2. 'many-to-many' constraint
  3. graph matching
  4. matching selection
  5. partial multi-label learning

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  • Research-article

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  • Fundamental Research Funds for the Central universities
  • Key R&D Program of Zhejiang Province
  • Beijing Natural Science Foundation
  • National Natural Science Foundation of China

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KDD '20
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  • (2025)Partial multi-label learning via label-specific feature correctionsScience China Information Sciences10.1007/s11432-023-4230-268:3Online publication date: 24-Jan-2025
  • (2024)Unbiased multi-label learning from crowdsourced annotationsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694289(54064-54081)Online publication date: 21-Jul-2024
  • (2024)Common-individual semantic fusion for multi-view multi-label learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/521(4715-4723)Online publication date: 3-Aug-2024
  • (2024)Partial Multi-label Learning Based On Near-Far Neighborhood Label Enhancement And Nonlinear GuidanceProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681300(3722-3731)Online publication date: 28-Oct-2024
  • (2024)Noisy Label Removal for Partial Multi-Label LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671677(3724-3735)Online publication date: 25-Aug-2024
  • (2024)Learning Accurate Label-Specific Features From Partially Multilabeled DataIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.324192135:8(10436-10450)Online publication date: Aug-2024
  • (2024)Negative Label and Noise Information Guided Disambiguation for Partial Multi-Label LearningIEEE Transactions on Multimedia10.1109/TMM.2024.340253426(9920-9935)Online publication date: 1-Jan-2024
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  • (2024)A ranking-based problem transformation method for weakly supervised multi-label learningPattern Recognition10.1016/j.patcog.2024.110505153(110505)Online publication date: Sep-2024
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