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Label enhancement with label-specific feature learning

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Abstract

Label distribution learning (LDL) is a novel machine learning paradigm. It addresses the problem of label ambiguity by emphasizing the relevance of each label to a particular instance. Unlike the simple logic vectors in single label learning (SLL) and multi-label learning (MLL), LDL assigns descriptive labels to each instance. Since it is often difficult to collect training sets with precise labels, label enhancement (LE) can be used to recover label distributions from logical labels. Most existing LE models construct label distributions based on a mapping from the feature space to the label space and include two main assumptions: (1) the relationship between features and labels is linear, and (2) all features are shared by all labels. However, in reality, the relationship between features and labels is not purely linear, and different labels may be determined by different features. To solve this problem, in this paper, we propose a new algorithm, that first maps features to a high-dimensional space to explore the nonlinear mapping relationship between features and labels, and then takes full advantage of the labels’ properties determined by specific features, while considering the correlation between labels. The experimental comparison with the existing algorithms verifies the effectiveness of the algorithm proposed in this paper.

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References

  1. Barutcuoglu Z, Schapire RE, Troyanskaya OG (2006) Hierarchical multi-label prediction of gene function. Bioinformatics 22(7):830–836

    Article  Google Scholar 

  2. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771

    Article  Google Scholar 

  3. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surv 47(3):1–38

    Article  Google Scholar 

  4. Herrera F, Charte F, Rivera AJ, Del Jesus M (2016) Multilabel classification. Springer, Berlin

    Book  Google Scholar 

  5. Triguero I, Vens C (2016) Labelling strategies for hierarchical multi-label classification techniques. Pattern Recogn 56:170–183

    Article  Google Scholar 

  6. Moyano JM, Gibaja EL, Cios KJ, Ventura S (2018) Review of ensembles of multi-label classifiers: models, experimental study and prospects. Inf Fusion 44:33–45

    Article  Google Scholar 

  7. Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734–1748

    Article  Google Scholar 

  8. Geng X, Yin C, Zhou ZH (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35(10):2401–2412

    Article  Google Scholar 

  9. Jia X, Zheng X, Li W, Zhang C, Li Z (2019) Facial emotion distribution learning by exploiting low-rank label correlations locally. In: IEEE conference on computer vision and pattern recognition, pp 9841–9850

  10. Hu D, Zhang H, Wu Z, Lin W, Li G, Shen D, Consortium UBCP, et al (2019) Deep granular feature-label distribution learning for neuroimaging-based infant age prediction. In: International conference on medical image computing and computer-assisted intervention, pp 149–157

  11. Xu N, Tao A, Geng X (2018) Label enhancement for label distribution learning. In: International joint conference on artificial intelligence, pp 2926–2932

  12. Shao R, Xu N, Geng X (2018) Multi-label learning with label enhancement. In: IEEE international conference on data mining, pp 437–446

  13. Huang J, Li G, Huang Q, Wu X (2015) Learning label specific features for multi-label classification. In: IEEE international conference on data mining, pp 181–190

  14. Ren T, Jia X, Li W, Chen L, Li Z (2019) Label distribution learning with label-specific features. In: international joint conference on artificial intelligence, pp 3318–3324

  15. Zheng X, Jia X, Li W (2018) Label distribution learning by exploiting sample correlations locally. In: AAAI conference on artificial intelligence, pp 4556–4563

  16. Shen W, Zhao K, Guo Y, Yuille A (2017) Label distribution learning forests. In: Neural information processing systems, pp 834–843

  17. Gao BB, Xing C, Xie CW, Wu J, Geng X (2017) Deep label distribution learning with label ambiguity. IEEE Trans Image Process 26(6):2825–2838

    Article  MathSciNet  Google Scholar 

  18. Jia X, Li W, Liu J, Zhang Y (2018) Label distribution learning by exploiting label correlations. In: AAAI conference on artificial intelligence, pp 3310–3317

  19. Wang J, Geng X (2019) Theoretical analysis of label distribution learning. In: AAAI conference on artificial intelligence, pp 5256–5263

  20. Su K, Geng X (2019) Soft facial landmark detection by label distribution learning. In: AAAI conference on artificial intelligence, pp 5008–5015

  21. Gao BB, Zhou HY, Wu J, Geng X (2018) Age estimation using expectation of label distribution learning. In: International joint conference on artificial intelligence, pp 712–718

  22. Geng X, Xia Y (2014) Head pose estimation based on multivariate label distribution. In: IEEE conference on computer vision and pattern recognition, pp 1837–1842

  23. Geng X, Luo L (2014) Multilabel ranking with inconsistent rankers. In: IEEE conference on computer vision and pattern recognition, pp 3742–3747

  24. Zhou D, Zhang X, Zhou Y, Zhao Q, Geng X (2016) Emotion distribution learning from texts. In: conference on empirical methods in natural language processing, pp 638–647

  25. Zhao S, Chen X, Yue X, Lin C, Xu P, Krishna R, Yang J, Ding G, Sangiovanni-Vincentelli AL, Keutzer K (2021) Emotional semantics-preserved and feature-aligned cyclegan for visual emotion adaptation. IEEE Trans Cybern 20:1–14

    Google Scholar 

  26. Zhao S, Zhao X, Ding G, Keutzer K (2018) Emotiongan: unsupervised domain adaptation for learning discrete probability distributions of image emotions. In: ACM international conference on multimedia, pp 1319–1327

  27. Zhu W, Jia X, Li W (2020) Privileged label enhancement with multi-label learning. In: International joint conference on artificial intelligence, pp 2376–2382

  28. El Gayar N, Schwenker F, Palm G (2006) A study of the robustness of Knn classifiers trained using soft labels. In: Conference on artificial neural networks in pattern recognition, pp 67–80

  29. Jiang X, Yi Z, Lv JC (2006) Fuzzy SVM with a new fuzzy membership function. Neural Comput Appl 15(3):268–276

    Article  Google Scholar 

  30. Zhang F, Jia X, Li W (2020) Tensor-based multi-view label enhancement for multi-label learning. In: International joint conference on artificial intelligence, pp 2369–2375

  31. Zhang M (2011) LIFT: multi-label learning with label-specific features. In: International joint conference on artificial intelligence, pp 1609–1614

  32. Zhang J, Li C, Cao D, Lin Y, Su S, Dai L, Li S (2018) Multi-label learning with label-specific features by resolving label correlations. Knowl-Based Syst 159:148–157

    Article  Google Scholar 

  33. Weng W, Lin Y, Wu S, Li Y, Kang Y (2018) Multi-label learning based on label-specific features and local pairwise label correlation. Neurocomputing 273:385–394

    Article  Google Scholar 

  34. Goldberger J, Hinton G, Roweis S, Salakhutdinov R (2004) Neighbourhood components analysis. In: Neural information processing systems, pp 513–520

  35. Lin Z, Ganesh A, Wright J, Wu L, Chen M, Ma Y (2009) Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. J Mar Biol Assoc UK 56(3):707–722

    Google Scholar 

  36. Yang J, Sun M, Sun X (2017) Learning visual sentiment distributions via augmented conditional probability neural network. In: AAAI conference on artificial intelligence, pp 224–230

  37. Li YK, Zhang ML, Geng X (2015) Leveraging implicit relative labeling-importance information for effective multi-label learning. In: IEEE international conference on data mining, pp 251–260

Download references

Acknowledgements

This paper is supported by the National Natural Science Foundation of China (Grant Nos. 61906090, 62176123).

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Authors and Affiliations

  1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Weiwei Li, Jin Chen & Peixue Gao

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Weiwei Li & Zhiqiu Huang

  3. Ministry Key Laboratory for Safety-Critical Software Development and Verification, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Weiwei Li & Zhiqiu Huang

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  1. Weiwei Li
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  2. Jin Chen
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  3. Peixue Gao
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  4. Zhiqiu Huang
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Correspondence to Weiwei Li.

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Li, W., Chen, J., Gao, P. et al. Label enhancement with label-specific feature learning. Int. J. Mach. Learn. & Cyber. 13, 2857–2867 (2022). https://doi.org/10.1007/s13042-022-01567-x

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  • DOI: https://doi.org/10.1007/s13042-022-01567-x

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