Abstract
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML problem is practical in real-world scenarios, as it is difficult and even impossible to obtain precisely labeled samples. Several PML solutions have been proposed to combat with the prone misled by the irrelevant labels concealed in the candidate labels, but they generally focus on the smoothness assumption in feature space or low-rank assumption in label space, while ignore the negative information between features and labels. Specifically, if two instances have largely overlapped candidate labels, irrespective of their feature similarity, their ground-truth labels should be similar; while if they are dissimilar in the feature and candidate label space, their ground-truth labels should be dissimilar with each other. To achieve a credible predictor on PML data, we propose a novel approach called PML-LFC (Partial Multi-label Learning with Label and Feature Collaboration). PML-LFC estimates the confidence values of relevant labels for each instance using the similarity from both the label and feature spaces, and trains the desired predictor with the estimated confidence values. PML-LFC achieves the predictor and the latent label matrix in a reciprocal reinforce manner by a unified model, and develops an alternative optimization procedure to optimize them. Extensive empirical study on both synthetic and real-world datasets demonstrates the superiority of PML-LFC.
This work is supported by NSFC (61872300 and 61871010).
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References
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. JMLR 7(11), 2399–2434 (2006)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Cour, T., Sapp, B., Taskar, B.: Learning from partial labels. JMLR 12(5), 1501–1536 (2011)
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: NeurIPS, pp. 681–687 (2002)
Fang, J.P., Zhang, M.L.: Partial multi-label learning via credible label elicitation. In: AAAI, pp. 3518–3525 (2019)
Gibaja, E., Ventura, S.: A tutorial on multilabel learning. ACM Comput. Surv. 47(3), 52 (2015)
Han, Y., Sun, G., Shen, Y., Zhang, X.: Multi-label learning with highly incomplete data via collaborative embedding. In: KDD, pp. 1494–1503 (2018)
Ji, S., Ye, J.: An accelerated gradient method for trace norm minimization. In: ICML, pp. 457–464 (2009)
Li, S.Y., Jiang, Y., Chawla, N.V., Zhou, Z.H.: Multi-label learning from crowds. TKDE 31(7), 1369–1382 (2019)
Li, Y.F., Hu, J.H., Jiang, Y., Zhou, Z.H.: Towards discovering what patterns trigger what labels. In: AAAI, pp. 1012–1018 (2012)
Liu, T., Tao, D.: Classification with noisy labels by importance reweighting. TPAMI 38(3), 447–461 (2016)
Natarajan, N., Dhillon, I.S., Ravikumar, P.K., Tewari, A.: Learning with noisy labels. In: NeurIPS, pp. 1196–1204 (2013)
Sun, L., Feng, S., Wang, T., Lang, C., Jin, Y.: Partial multi-label learning by low-rank and sparse decomposition. In: AAAI, pp. 5016–5023 (2019)
Sun, Y.Y., Zhang, Y., Zhou, Z.H.: Multi-label learning with weak label. In: AAAI, pp. 593–598 (2010)
Tan, Q., Liu, Y., Chen, X., Yu, G.: Multi-label classification based on low rank representation for image annotation. Remote Sens. 9(2), 109 (2017)
Tan, Q., Yu, G., Domeniconi, C., Wang, J., Zhang, Z.: Incomplete multi-view weak-label learning. In: IJCAI, pp. 2703–2709 (2018)
Tu, J., Yu, G., Domeniconi, C., Wang, J., Xiao, G., Guo, M.: Multi-label answer aggregation based on joint matrix factorization. In: ICDM, pp. 517–526 (2018)
Wang, C., Yan, S., Zhang, L., Zhang, H.J.: Multi-label sparse coding for automatic image annotation. In: CVPR, pp. 1643–1650 (2009)
Wang, H., Liu, W., Zhao, Y., Zhang, C., Hu, T., Chen, G.: Discriminative and correlative partial multi-label learning. In: IJCAI, pp. 2703–2709 (2019)
Wu, B., Jia, F., Liu, W., Ghanem, B., Lyu, S.: Multi-label learning with missing labels using mixed dependency graphs. IJCV 126(8), 875–896 (2018)
Xie, M.K., Huang, S.J.: Partial multi-label learning. In: AAAI, pp. 4302–4309 (2018)
Xu, L., Wang, Z., Shen, Z., Wang, Y., Chen, E.: Learning low-rank label correlations for multi-label classification with missing labels. In: ICDM, pp. 1067–1072 (2014)
Yu, G., et al.: Feature-induced partial multi-label learning. In: ICDM, pp. 1398–1403 (2018)
Yu, G., Fu, G., Wang, J., Zhu, H.: Predicting protein function via semantic integration of multiple networks. TCBB 13(2), 220–232 (2016)
Zhang, J., Wu, X.: Multi-label inference for crowdsourcing. In: KDD, pp. 2738–2747 (2018)
Zhang, M.L., Yu, F., Tang, C.Z.: Disambiguation-free partial label learning. TKDE 29(10), 2155–2167 (2017)
Zhang, M.L., Zhang, K.: Multi-label learning by exploiting label dependency. In: KDD, pp. 999–1008 (2010)
Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. TKDE 26(8), 1819–1837 (2014)
Zhang, Y., Zhou, Z.H.: Multilabel dimensionality reduction via dependence maximization. TKDD 4(3), 14 (2010)
Zhu, Y., Kwok, J.T., Zhou, Z.H.: Multi-label learning with global and local label correlation. TKDE 30(6), 1081–1094 (2017)
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Yu, T., Yu, G., Wang, J., Guo, M. (2020). Partial Multi-label Learning with Label and Feature Collaboration. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12112. Springer, Cham. https://doi.org/10.1007/978-3-030-59410-7_41
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