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GRNet: Graph-based remodeling network for multi-view semi-supervised classification
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DOI：10.1016/j.patrec.2021.08.008.png)
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Multi-view semi-supervised classification (MSSC) focuses on exploring information from multiple views of labeled and unlabeled data to boost classification performance. However, most of the existing meth-ods build models for each view individually, therefore, the potential relationship between different views can't be fully explored. Additionally, they either focus on the correlations for consistency or maximize the independence for complementarity. Consistency and complementarity are equally important for multi-view learning. Therefore, this work proposes a novel Graph-based Remodeling Network for MSSC (GRNet), which can explore the potential relationship of multiple views and balance the consistency and com-plementation adaptively. Specifically, this model integrates multiple views and then generates reformed pseudo views by an attention-based ensemble learning strategy. Moreover, to exploit the information of unlabeled data, this work introduces graph regularization for such a goal. Extensive experiments on sev-eral datasets demonstrate the effectiveness and efficiency of the proposed method. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Multi-view semi-supervised learning
Remodeling network
Consistency and complementation
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