Abstract
Multi-view subspace clustering has been widely applied in practical applications. It fuses complementary information across multiple views and treats all samples of a view as a set of bases of a generalized subspace. Meanwhile, it assumes that an instance has all features corresponding to all views. However, each view may lose some features due to the malfunction, which results in an incomplete multi-view dataset. This paper presents an incomplete multi-view subspace clustering with adaptive instance-sample mapping and deep feature fusion algorithm (IMDF). Owing to the good performance of attention mechanism, we fuse deep features adaptively extracted by convolutional neural networks in a weighted way to integrate abundant information from distinctive views, reduce redundancy between features, and generalize a robust and compact representation before self-representation stage. We jointly process feature training and clustering for a specific task to weaken the sensitivity of model to pre-extracted features. At the same time, we propose a modified weighted view-specific instance-sample mapping strategy to solve the inconsistent dimensions problem induced by different numbers of samples of distinct views in the process of learning a common representation in a unified latent subspace. Experimental results demonstrate that our method outperforms five state-of-the-art methods on various real-world datasets including images and documents.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (NSFC) [grant numbers 61502175, 61273295]; and the Natural Science Foundation of Guangdong Province [grant number 2020A1515010699].
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Xie, M., Ye, Z., Pan, G. et al. Incomplete multi-view subspace clustering with adaptive instance-sample mapping and deep feature fusion. Appl Intell 51, 5584–5597 (2021). https://doi.org/10.1007/s10489-020-02138-9
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DOI: https://doi.org/10.1007/s10489-020-02138-9