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Efficient multi-view clustering networks

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Abstract

In the last decade, deep learning has made remarkable progress on multi-view clustering (MvC), with existing literature adopting a broad target to guide the network learning process, such as minimizing the reconstruction loss. However, despite this strategy being effective, it lacks efficiency. Hence, in this paper, we proposed a novel framework, entitled Efficient Multi-view Clustering Networks (EMC-Nets), which guarantees the network’s learning efficiency and produces a common discriminative representation from multiple sources. Specifically, we developed an alternating process, involving an approximation and an instruction process, which effectively stimulate the process of multi-view feature fusion to force network to learn a discriminative common representation. The approximation process employs a standard clustering algorithm, i.e., k-means, to generate pseudo labels corresponding to the current common representation, and then it leverages the pseudo labels to force the network to approximate a reasonable cluster distribution. Considering the instruction process, it aims to provide a correct learning direction for the approximation process and prevent the network from obtaining trivial solutions. Experiment results on four real-world datasets demonstrate that the proposed method outperforms state-of-the-art methods. Our source code will be available soon at https://github.com/Guanzhou-Ke/EMC-Nets.

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Acknowledgements

This work was supported by Natural Science Foundation of Guangdong Province, China (No. 2020A1515011468; 2016A0303010003); Guangdong University Scientific Research Project, China (No. 2019KTSCX189; 2018KTSCX235; 2018WZDXM014); Joint Research and Development Fund of Wuyi University and Hong Kong and Macau (2019WGALH21).

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Authors and Affiliations

  1. Department of Intelligent Manufacturing, Wuyi University, 529020, Jiangmen, China

    Guanzhou Ke, Zhiyong Hong, Wenhua Yu, Xin Zhang & Zeyi Liu

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  1. Guanzhou Ke
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  2. Zhiyong Hong
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  3. Wenhua Yu
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  4. Xin Zhang
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  5. Zeyi Liu
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Correspondence to Zhiyong Hong.

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Appendix: Network architectures

Appendix: Network architectures

In this section, we report the details of the network architecture, includes network layer, input and output size, hyper-parameters of the layer, and the total number of parameters, used in the experiments. Note that all the layer of network are used Pytorch-style API. For the fully-connected (linear) layer, we use bias as default, and we report the head and the dimension of feedforward (dff) of the TransformerEncoder (Tables 7, 8, 9, 10).

Table 7 EMC-Nets in the BDGP experiments
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Table 8 EMC-Nets in the HW experiments
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Table 9 EMC-Nets in the CCV experiments
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Table 10 EMC-Nets in the MNIST experiments
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Ke, G., Hong, Z., Yu, W. et al. Efficient multi-view clustering networks. Appl Intell 52, 14918–14934 (2022). https://doi.org/10.1007/s10489-021-03129-0

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