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Structure regularized self-paced learning for robust semi-supervised pattern classification

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Abstract

Semi-supervised classification is a hot topic in pattern recognition and machine learning. However, in presence of heavy noise and outliers, the unlabeled training data could be very challenging or even misleading for the semi-supervised classifier. In this paper, we propose a novel structure regularized self-paced learning method for semi-supervised classification problems, which can efficiently learn partially labeled training data sequentially from the simple to the complex ones. The proposed formulation consists of three components: a cost function defined by a mixture of losses, a functional complexity regularizer, and a self-paced regularizer; and the corresponding optimization algorithm involves three iterative steps: classifier updating, sample importance calculating, and pseudo-labeling. In the proposed method, the cost function for classifier updating and sample importance calculating is defined as a combination of the label fitting loss and manifold smoothness loss. Then, the importance of the pseudo-labeled and unlabeled samples is adaptively calculated by the novel cost. Unlabeled samples with high importance values are pseudo-labeled with their current predictions. In this way, labels are efficiently propagated from the labeled samples to the unlabeled ones in the robust self-paced manner. Experimental results on several benchmark data sets are provided to show the effectiveness of the proposed method.

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Notes

  1. http://cbcl.mit.edu/software-datasets.

  2. http://www.iis.ee.ic.ac.uk/icvl/code.htm.

  3. http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation (NNSF) of China [Grant Numbers: 61503263, 61772373, 61772374], in part by the Zhejiang Provincial Natural Science Foundation [Grant Numbers: LY15F030011, LY17F030004], in part by the Project of science and technology plans of Wenzhou City [Grant Number: G20160002].

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Authors and Affiliations

  1. School of Statistics, Capital University of Economics and Business, Beijing, 100070, China

    Nannan Gu

  2. School of Economics, Beijing Technology and Business University, Beijing, 100048, China

    Pengying Fan

  3. College of Mathematics and Information Science, Wenzhou University, Wenzhou, 325035, China

    Mingyu Fan & Di Wang

Authors
  1. Nannan Gu
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  2. Pengying Fan
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  3. Mingyu Fan
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  4. Di Wang
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Correspondence to Mingyu Fan.

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Gu, N., Fan, P., Fan, M. et al. Structure regularized self-paced learning for robust semi-supervised pattern classification. Neural Comput & Applic 31, 6559–6574 (2019). https://doi.org/10.1007/s00521-018-3478-1

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