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Multi-label optimal margin distribution machine

  • Zhi-Hao Tan
  • Peng Tan
  • Yuan JiangEmail author
  • Zhi-Hua Zhou
Article
  • 32 Downloads
Part of the following topical collections:
  1. Special Issue of the ACML 2019 Journal Track

Abstract

Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Moreover, empirical study presents the best margin distribution and verifies the fast convergence of our method.

Keywords

Optimal margin distribution machine Multi-label learning Support vector machine Margin theory 

Notes

Acknowledgements

This research was supported by the National Key R&D Program of China (2018YFB1004300), NSFC (61673201), and the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Copyright information

© The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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