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An adaptive weighted self-representation method for incomplete multi-view clustering

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Abstract

For multi-view data in reality, part of its elements may be missing because of human or machine error. Incomplete multi-view clustering (IMC) clusters the incomplete multi-view data according to the characters of various views of the instances. Recently, IMC has attracted much attention and many related methods have been proposed. However, the existing approaches still need to be developed and innovated in the following aspects: (1) current methods only consider the differences of different views, while the different influences of instances, as well as distinguishes between missing values and completed values are ignored. (2) The updating scheme for weighting matrix in adaptive weighted algorithms usually relies on an optimization sub-problem, whose optimal solution may not be easy to achieve. (3) The adaptive weighted subspace algorithms that can recover the incomplete data are anchor types. The randomness of the anchor matrix may cause unreliability. To tackle these limitations, we propose an adaptive weighted self-representation (AWSR) subspace method for IMC. The AWSR method tunes the weighting matrix adaptively in accordance with the views of different instances and the recovery process of the missing values. The low rank and smoothness constraints on the representation matrix make the subspace reveal the underlying features of the dataset accurately. We also analyze the convergence property of the block coordinate method for our optimization model theoretically. Numerical performance on five real-world data shows that the AWSR method is effective and delivers superior results when compared to other eight widely-used approaches considering the clustering accuracy (ACC), normalized mutual information (NMI) and Purity.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Notes

  1. http://cam-orl.co.uk/facedatabase.html.

  2. https://www.di.ens.fr/willow/research/stillactions/.

  3. http://mlg.ucd.ie/datasets/bbc.html.

  4. http://mlg.ucd.ie/aggregation/.

  5. https://github.com/cswanghao/gbs/blob/.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant no. 12326302, no. 62073087].

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Authors and Affiliations

  1. School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, 510520, China

    Lishan Feng

  2. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Guoxu Zhou

  3. School of Mathematics and Statistics, Center for Mathematics and Interdisciplinary Sciences, Guangdong University of Technology, Guangzhou, 510520, China

    **gya Chang

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  1. Lishan Feng
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  2. Guoxu Zhou
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  3. **gya Chang
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Contributions

Conceptualization: [Lishan Feng], [**gya Chang]; Software: [Lishan Feng]; Validation: [Lishan Feng], [Guoxu Zhou], [**gya Chang]; Formal analysis: [Lishan Feng]; Data curation: [Lishan Feng]; Writing—original draft: [Lishan Feng]; Preparation: [Lishan Feng]; Visualization: [Lishan Feng]; Project administration: [Lishan Feng]; Investigation: [Guoxu Zhou], [**gya Chang]; Resources: [Guoxu Zhou], [**gya Chang]; Supervision:[Guoxu Zhou], [**gya Chang]; Funding acquisition: [Guoxu Zhou], [**gya Chang]; Methodology: [**gya Chang]; Writing—review & editing: [**gya Chang]. All authors have read and agreed to the published version of the manuscript.

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Correspondence to **gya Chang.

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Feng, L., Zhou, G. & Chang, J. An adaptive weighted self-representation method for incomplete multi-view clustering. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02163-x

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