Abstract
In partial label learning, each training instance is assigned with a set of candidate labels, among which only one is correct. An intuitive strategy to learn from such ambiguous data is disambiguation. Existing methods following such strategy either identify the ground-truth label via treating each candidate label equally or disambiguate the candidate label set via assuming latent variable and optimizing it iteratively. In this paper, we propose a novel two-stage method called partial label learning via low-rank representation and label propagation, where instance similarity and label confidence are taken into consideration to improve the disambiguation ability of the model. In the first stage, we first build the global instance–similarity relationship via low rank representation and sparse constraint and then obtain the accurate instance-label assignments via iterative label propagation strategy. In the second stage, we utilize the Adaboost.R2 to make prediction for unseen instances, where CART is incorporated as the weak classifier. Extensive experiments on the artificial and real-world data sets demonstrate that the proposed method can achieve superior or comparable performance than the comparing state-of-the-art approaches.
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References
Barnard K, Duygulu P, Forsyth D, Freitas Nd, Blei DM, Jordan MI (2003) Matching words and pictures. J Mach Learn Res 3(Feb):1107–1135
Breiman L (2017) Classification and regression trees. Routledge, Abingdon
Briggs F, Fern XZ, Raich R (2012) Rank-loss support instance machines for miml instance annotation. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 534–542
Briggs F, Fern X, Raich R (2012) Rank-loss support instance machines for miml instance annotation. In: International conference on knowledge discovery and data mining, pp. 534–542
Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM (JACM) 58(3):11
Chen Y, Patel V, Chellappa R, Phillips P (2014) Ambiguously labeled learning using dictionaries. IEEE Trans Inf Forensics Secur 9(12):2076–2088
Chen CH, Patel VM, Chellappa R (2018) Learning from ambiguously labeled face images. IEEE Trans Pattern Anal Mach Intell 40:1653–1667
Cour T, Sapp B, Taskar B (2011) Learning from partial labels. IEEE Trans Knowl Data Eng 12(5):1501–1536
Gong C, Liu T, Tao D, Fu K, Tu E, Yang J (2015) Deformed graph Laplacian for semisupervised learning. IEEE Trans Neural Netw Learn Syst 26(10):2261–2274
Gong C, Tao D, Maybank SJ, Liu W, Kang G, Yang J (2016) Multi-modal curriculum learning for semi-supervised image classification. IEEE Trans Image Process 25(7):3249–3260
Gong C, Tao D, Liu W, Liu L, Yang J (2017) Label propagation via teaching-to-learn and learning-to-teach. IEEE Trans Neural Netw Learn Syst 28(6):1452–1465
Gong C, Liu T, Tang Y, Jian Y, Jie Y, Tao D (2017) A regularization approach for instance-based superset label learning. IEEE Trans Cybern 48(3):967–978
Guillaumin M, Verbeek J, Schmid C (2010) Multiple instance metric learning from automatically labeled bags of faces. In: European conference on computer vision, pp. 634–647
Huang W, Feng S (2018) Partial label learning via low rank representation and label propagation. In: International conference on internet multimedia computing and service, pp. 1–7
Hullermeier E, Beringer J (2005) Learning from ambiguously labeled examples. Int Symp Intell Data Anal 10(5):168–179
Jin R, Ghahramani Z (2003) Learning with multiple labels. In: Advances in neural information processing systems, pp. 921–928
Liu L, Dietterich TG (2012) A conditional multinomial mixture model for superset label learning. In: Advances in neural information processing systems, pp. 548–556
Nguyen N, Caruana R (2008) Classification with partial labels. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp. 551–559
Tang C, Zhang M (2017) Confidence-rated discriminative partial label learning. In: AAAI conference on artificial intelligence, pp. 2611–2617
Wang F, Zhang C (2008) Label propagation through linear neighborhoods. IEEE Trans Knowl Data Eng 20(1):55–67
Wu X, Zhang ML (2018) Towards enabling binary decomposition for partial label learning. In: Proceedings of the 27th international joint conference on artificial intelligence
Xie M, Huang S (2018) Partial multi-label learning. In: AAAI conference on artificial intelligence, pp. 1
Yu F, Zhang M (2017) Maximum margin partial label learning. Mach Learn 106(4):573–593
Yu J, Rui Y, Tang YY, Tao D (2014) High-order distance-based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442
Yu J, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779
Zeng Z, Xiao S, Jia K, Chan T, Gao S, Xu D, Ma Y (2013) Learning by associating ambiguously labeled images. In: IEEE conference on computer vision and pattern recognition, pp. 708–715
Zhang M, Yu F (2015) Solving the partial label learning problem: an instance-based approach. In: International joint conference on artificial intelligence, pp. 4048–4054
Zhang M, Zhou B, Liu X (2016) Partial label learning via feature-aware disambiguation. In: International conference on knowledge discovery and data mining, pp. 1335–1344
Zhou Y, He J, Gu H (2016) Partial label learning via Gaussian processes. IEEE Trans Cybern 47(12):4443–4450
Zhang M, Yu F, Tang C (2017) Disambiguation-free partial label learning. IEEE Trans Knowl Data Eng 29(10):2155–2167
Zhuang L, Gao H, Lin Z, Ma Y, Zhang X, Yu N (2012) Non-negative low rank and sparse graph for semi-supervised learning. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR), IEEE, pp. 2328–2335
Acknowledgements
This work was supported in part by the Fundamental Research Funds for the Central universities (2018YJS033, 2019JBM020), in part by the National Natural Science Foundation of China (Nos. 61872032), in part by the Key R&D Program of Zhejiang Province (Nos. 2019C01068).
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Lyu, G., Feng, S., Huang, W. et al. Partial label learning via low-rank representation and label propagation. Soft Comput 24, 5165–5176 (2020). https://doi.org/10.1007/s00500-019-04269-9
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DOI: https://doi.org/10.1007/s00500-019-04269-9