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Partial Label Learning via Conditional-Label-Aware Disambiguation

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Abstract

Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels, among which only one is the ground-truth label. This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling. Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints, our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.

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References

  1. Cour T, Sapp B, Taskar B. Learning from partial labels. Journal of Machine Learning Research, 2011, 12: 1501-1536.

    MathSciNet  MATH  Google Scholar 

  2. Chen Y C, Patel V M, Chellappa R, Phillips P J. Ambiguously labeled learning using dictionaries. IEEE Transactions on Information Forensics and Security, 2014, 9(12): 2076-2088. DOI: https://doi.org/10.1109/TIFS.2014.2359642.

    Article  Google Scholar 

  3. Zhang M L, Zhou B B, Liu X Y. Partial label learning via feature-aware disambiguation. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.1335-1344. DOI: 10.1145/2939672.2939788.

  4. Dong X B, Yu Z W, Cao W M, Shi Y F, Ma Q L. A survey on ensemble learning. Frontiers of Computer, 2020, 14(2): 241-258. DOI: https://doi.org/10.1007/s11704-019-8208-z.

    Article  Google Scholar 

  5. Zhang M L, Li Y K, Liu X Y. Binary relevance for multilabel learning: An overview. Frontiers of Computer Science, 2018, 12(2): 191-202. DOI: https://doi.org/10.1007/s11704-017-7031-7.

    Article  Google Scholar 

  6. Xu S, Yang M, Zhou Y, Zheng R, Liu W, He J. Partial label metric learning by collapsing classes. International Journal of Machine Learning and Cybernetics, 2020, 11: 2453-2460. DOI: https://doi.org/10.1007/s13042-020-01129-z.

    Article  Google Scholar 

  7. Luo J, Orabona F. Learning from candidate labeling sets. In Proc. the 24th Annual Conference on Neural Information Processing Systems, December 2010, pp.1504-1512.

  8. Zeng Z, Xiao S, Jia K, Chan T H, Gao S, Xu D, Ma Y. Learning by associating ambiguously labeled images. In Proc. the 2013 IEEE Conference on Computer Vision and Pattern Recognition, June 2013, pp.708-715. DOI: 10.1109/CVPR.2013.97.

  9. Liu L, Dietterich T G. A conditional multinomial mixture model for superset label learning. In Proc. the 25th International Conference on Neural Information Processing Systems, December 2012, pp.548-556.

  10. Zhang M L, Yu F, Tang C Z. Disambiguation free partial label learning. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2155-2167. DOI: https://doi.org/10.1109/TKDE.2017.2721942.

    Article  Google Scholar 

  11. Hüllermeier E, Beringer J. Learning from ambiguously labeled examples. Intelligent Data Analysis, 2006, 10(5): 419-439. DOI: https://doi.org/10.3233/IDA-2006-10503.

    Article  MATH  Google Scholar 

  12. Zhang M L, Yu F. Solving the partial label learning problem: An instance-based approach. In Proc. the 24th International Joint Conference on Artificial Intelligence, July 2015, pp.4048-4054.

  13. Jin R, Ghahramani Z. Learning with multiple labels. In Proc. the 15th International Conference on Neural Information Processing Systems, January 2003, pp.921-928.

  14. Nguyen N, Caruana R. Classification with partial labels. In Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2008, pp.551-559. DOI: 10.1145/1401890.1401958.

  15. Feng L, An B. Leveraging latent label distributions for partial label learning. In Proc. the 27th International Joint Conference on Artificial Intelligence, July 2018, pp.2107-2113. DOI: 10.24963/ijcai.2018/291.

  16. Wang D B, Li L, Zhang M L. Adaptive graph guided disambiguation for partial label learning. In Proc. the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, July 2019, pp.83-91. DOI: 10.1145/3292500.3330840.

  17. Tang C Z, Zhang M L. Confidence-rated discriminative partial label learning. In Proc. the 31st AAAI Conference on Artificial Intelligence, February 2017, pp. 2611-2617.

  18. Yu F, Zhang M L. Maximum margin partial label learning. Machine Learning, 2017, 106(4): 573-593. DOI: https://doi.org/10.1007/s10994-016-5606-4.

    Article  MathSciNet  MATH  Google Scholar 

  19. Xu N, Lv J, Geng X. Partial label learning via label enhancement. In Proc. the 33rd AAAI Conference on Artificial Intelligence, January 27–February 1, 2019. DOI: 10.1609/aaai.v33i01.33015557.

  20. Feng L, An B. Partial label learning by semantic difference maximization. In Proc. the 28th International Joint Conference on Artificial Intelligence, August 2019, pp.2294-2300. DOI: 10.24963/ijcai.2019/318.

  21. Xie M K, Huang S J. Partial multi-label learning. In Proc. the 32nd AAAI Conference on Artificial Intelligence, February 2018, pp.4302-4309.

  22. Yu G, Chen X, Domeniconi C, Wang J, Li Z, Zhang Z, Wu X. Feature-induced partial multilabel learning. In Proc. the 2018 IEEE International Conference on Data Mining, November 2018, pp.1398-1403. DOI: 10.1109/ICDM.2018.00192.

  23. Fang J P, Zhang M L. Partial multi-label learning via credible label elicitation. In Proc. the 33rd AAAI Conference on Artificial Intelligence, January 27–February 1, 2019, pp.3518-3525. DOI: 10.1609/aaai.v33i01.33013518.

  24. Sun L, Feng S, Wang T, Lang C, Jin Y. Partial multilabel learning by low-rank and sparse decomposition. In Proc. the 33rd AAAI Conference on Artificial Intelligence, January 27–February 1, 2019, pp.5016-5023. DOI: 10.1609/aaai.v33i01.33015016.

  25. Sun L, Ge H W, Kang W J. Non-negative matrix factorization based modeling and training algorithm for multi-label learning. Frontiers of Computer Science, 2019, 13(6): 1243-1254. DOI: https://doi.org/10.1007/s11704-018-7452-y.

    Article  Google Scholar 

  26. Schölkopf B, Smola A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (1st edition). The MIT Press, 2001.

  27. Yuille A L, Rangarajan A. The concave-convex procedure. Neural Computation, 2003, 15(4): 915-936. DOI: https://doi.org/10.1162/08997660360581958.

    Article  MATH  Google Scholar 

  28. Sriperumbudur B K, Torres D A, Lanckriet G R. A majorization-minimization approach to the sparse generalized eigenvalue problem. Machine Learning, 2011, 85(1/2): 3-39. DOI: https://doi.org/10.1007/s10994-010-5226-3.

    Article  MathSciNet  MATH  Google Scholar 

  29. Gong C, Liu T, Tang Y, Yang J, Yang J, Tao D. A regularization approach for instance-based superset label learning. IEEE Transactions on Cybernetics, 2018, 48(3): 967-978. DOI: https://doi.org/10.1109/TCYB.2017.2669639.

    Article  Google Scholar 

  30. Sriperumbudur B K, Lanckriet G R. On the convergence of the concave-convex procedure. In Proc. the 23rd International Conference on Neural Information Processing Systems, December 2009, pp.1759-1767.

  31. Guillaumin M, Verbeek J, Schmid C. Multiple instance metric learning from automatically labeled bags of faces. In Proc. the 11th European Conference on Computer Vision, September 2010, pp.634-647. DOI: 10.1007/978-3-642-15549-9 46.

  32. Briggs F, Fern X Z, Raich R. Rank-loss support instance machines for MIML instance annotation. In Proc. the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2012, pp.534-542. DOI: 10.1145/2339530.2339616.

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Authors and Affiliations

  1. Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education, Beijing, 100087, China

    Peng Ni, Su-Yun Zhao, Zhi-Gang Dai, Hong Chen & Cui-Ping Li

  2. School of Information, Renmin University of China, Beijing, 100087, China

    Peng Ni, Su-Yun Zhao, Zhi-Gang Dai, Hong Chen & Cui-Ping Li

Authors
  1. Peng Ni
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  2. Su-Yun Zhao
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  3. Zhi-Gang Dai
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  4. Hong Chen
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  5. Cui-Ping Li
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Correspondence to Su-Yun Zhao.

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Ni, P., Zhao, SY., Dai, ZG. et al. Partial Label Learning via Conditional-Label-Aware Disambiguation. J. Comput. Sci. Technol. 36, 590–605 (2021). https://doi.org/10.1007/s11390-021-0992-x

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