Abstract
Multi-instance multi-label learning (MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels. During the past few years, many MIML algorithms have been developed and many applications have been described. However, there lacks theoretical exploration to the learnability of MIML. In this paper, through proving a generalization bound for multi-instance single-label learner and viewing MIML as a number of multi-instance single-label learning subtasks with the correlation among the labels, we show that the MIML hypothesis class constructed from a multi-instance single-label hypothesis class is PAC-learnable.
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References
Zhou Z H, Zhang ML.Multi-instance multi-label learning with application to scene classification. In: Schölkopf B, Platt J C, Hoffman T, eds. Proceedings of the 20th Annual Conference on Neural Information Processing Systems, 2006 Dec 4–7, Vancouver, Canada: MIT Press, 2006. 1609–1616
Zhou Z H, Zhang M L, Huang S J, et al. Multi-instance multi-label learning. Artif Intell, 2012, 176: 2291–2320
Zhang M L, Zhou Z H. M3MIML: A maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining, 2008 Dec 15–19, Pisa, Italy. IEEE Computer Society, 2008. 688–697
Zha Z J, Hua X S, Mei T, et al. Joint multi-label multi-instance learning for image classification. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, 2008 Jun 24–26, Anchorage, AK. IEEE Computer Society, 2008. 1–8
Yang S H, Zha H, Hu B G. Dirichlet-bernoulli alignment: A generative model for multi-class multi-label multi-instance corpora. In: Bengio Y, Schuurmans D, Lafferty J, et al., eds. Proceedings of the 23th Annual Conference on Neural Information Processing Systems, 2009 Dec 7–10, Vancouver, Canada: MIT Press, 2009. 2143–2150
Nguyen N. A new SVM approach to multi-instance multi-label learning. In: Webb G I, Liu B, Zhang C, et al., eds. Proceeding of the 10th IEEE International Conference on Data Mining, 2010 Dec 14–17, Sydney, Australia. IEEE Computer Society, 2010. 384–392
Xu X S, Xue X, Zhou Z H. Ensemble multi-instance multi-label learning approach for video annotation task. In: Candan K S, Panchanathan S, Prabhakaran B, et al., eds. Proceedings of the 19th ACM International Conference on Multimedia, 2011 Nov 28–Dec 1, Scottsdale, AZ. ACM, 2011. 1153–1156
Li Y X, Ji S, Kumar S, et al. Drosophila gene expression pattern annotation through multi-instance multi-label learning. IEEE ACM T Comput Bi, 2012, 9: 98–112
Sabato S, Tishby N. Homogenous multi-instance learning with arbitrary dependence. In: Proceeding of the 22nd Annual Conference on Learning Theory, 2009 Jun 18–21, Quebec, Canada: Omnipress, 2009. 93–104
Dietterich T G, Lathrop R H, Lozano-Pérez T, et al. Solving the multiple-instance problem with axis-parallel rectangles. Artif Intell, 1997, 89: 31–71
Foulds J R, Frank E. A review of multi-instance learning assumptions. Knowl Eng Rev, 2010, 25: 1–25
Schapire R E, Freund Y, Bartlett P, et al. Boosting the margin: A new explanation for the effectiveness of voting methods. Ann Stat, 1998, 26: 1651–1686
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Wang, W., Zhou, Z. Learnability of multi-instance multi-label learning. Chin. Sci. Bull. 57, 2488–2491 (2012). https://doi.org/10.1007/s11434-012-5133-z
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DOI: https://doi.org/10.1007/s11434-012-5133-z