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Regularized partial least squares for multi-label learning

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Abstract

In reality, data objects often belong to several different categories simultaneously, which are semantically correlated to each other. Multi-label learning can handle and extract useful information from such kind of data effectively. Since it has a great variety of potential applications, multi-label learning has attracted widespread attention from many domains. However, two major challenges still remain for multi-label learning: high dimensionality and correlations of data. In this paper, we address the problems by using the technique of partial least squares (PLS) and propose a new multi-label learning method called rPLSML (regularized Partial Least Squares for Multi-label Learning). Specifically, we exploit PLS discriminant analysis to identify a latent and common space from the variable and label spaces of data, and then construct a learning model based on the latent space. To tackle the multi-collinearity problem raised from the high dimensionality, a \(\ell _2\)-norm penalty is further exerted on the optimization problem. The experimental results on public data sets show that rPLSML has better performance than the state-of-the-art multi-label learning algorithms.

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Notes

  1. http://www.computationalmedicine.org/challenge/.

  2. http://mlkd.csd.auth.gr/multilabel.html.

  3. http://www.public.asu.edu/~jye02/Software/CCA/index.html.

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Acknowledgments

The authors are grateful to the anonymous referees for their valuable comments and suggestions, which can substantially improve this paper. This work was partially supported by the National NSF of China (61572443, 61272007, 61170109 and 61170108), the NSF of Zhejiang province (LY14F020008 and LY14F020012).

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Authors and Affiliations

  1. Department of Computer Science, Zhejiang Normal University, Jinhua, China

    Huawen Liu, Jianmin Han, Zhongyu Chen & Zhonglong Zheng

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia

    Zongjie Ma

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  1. Huawen Liu
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  2. Zongjie Ma
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  3. Jianmin Han
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Correspondence to Huawen Liu.

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Liu, H., Ma, Z., Han, J. et al. Regularized partial least squares for multi-label learning. Int. J. Mach. Learn. & Cyber. 9, 335–346 (2018). https://doi.org/10.1007/s13042-016-0500-8

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