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Incomplete multi-view partial multi-label learning

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Abstract

Partial multi-label learning is of great significant interest due to accurate supervision is difficult to be obtained. Recently, multi-view learning has been developed to deal with partial multi-label learning tasks. Although few multi-view partial multi-label learning methods have been proposed, all of them are designed under the full-view assumption. However, due to the difficulties in multi-view data collection, some views may not contain complete information in real task. The appearance of missing views will affect the performance of traditional partial multi-label learning algorithms. To solve this problem, we propose a novel I ncomplete M ulti-V iew P artial M ulti-L abel learning (IMVPML) framework which makes use of incomplete multi-view feature representation and utilizes the low-rank and sparse decomposition scheme to remove the noisy labels. Specifically, we first learn a shared subspace across heterogenous incomplete views. Secondly, we utilize the low-rank and sparse decomposition scheme to obtain the ground-truth labels. Thirdly, we introduce a graph Laplacian regularization to constrain the ground-truth labels and impose orthogonality constraints on the correlations between subspace. Finally, a predictive model is learned by shared subspace and disambiguation labels. Enormous experimental results demonstrate that the proposed method can achieve competitive performance in solving the problem of incomplete multi-view partial multi-label learning.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61872032), the Beijing Natural Science Foundation (No. 4202058), the Fundamental Research Funds for the Central universities (2019JBM020), and in part by the National Key Research and Development Project (No. 2018AAA0100300).

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Xinyuan Liu & Songhe Feng

  2. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

    Lijuan Sun

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  1. Xinyuan Liu
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  2. Lijuan Sun
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  3. Songhe Feng
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Correspondence to Songhe Feng.

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Xinyuan Liu and Lijuan Sun contributed the work equally.

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Liu, X., Sun, L. & Feng, S. Incomplete multi-view partial multi-label learning. Appl Intell 52, 3289–3302 (2022). https://doi.org/10.1007/s10489-021-02606-w

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