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Multi-view spectral clustering based on adaptive neighbor learning and low-rank tensor decomposition

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Abstract

Aiming at the problem that traditional multi-view clustering algorithms only focus on shared information in multi-views and ignore the unique information and high-order correlations of each view, in this paper, a multi-view spectral clustering algorithm based on adaptive neighbor learning and low-rank tensor decomposition (ANLTSC) is proposed. Specifically, in the ANLTSC model, we adopt the adaptive neighbor graph construction method to learn the similarity graph of each view and calculate the transition probability matrix corresponding to each view, so as to reveal the class properties between samples more accurately. Then, transition probability matrices are stacked into a tensor for mining high-order correlations between multi-view data. The noise between different views is effectively filtered through tensor decomposition, and an intrinsically low-rank tensor is obtained for final clustering. Finally, experiments are carried out on seven benchmark datasets, the results show that the proposed ANLTSC algorithm achieves the best clustering results on most multi-view datasets.

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Data Availability

Data openly available in a public repository.

Notes

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

  2. https://cs.nyu.edu/roweis/data.html.

  3. http://archive.ics.uci.edu/ml/datasets/Multiple+Features.

  4. https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61866033), the Gansu Provincial Department of Education University Teachers Innovation Fund Project (No.2023B-056), the Introduction of Talent Research Project of Northwest Minzu University (No. xbmuyjrc201904), and the Fundamental Research Funds for the Central Universities of Northwest Minzu University (No.31920220019, 31920220130).

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Authors and Affiliations

  1. Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Chinese National Information Technology Research Institute, Northwest Minzu University, Lanzhou, Gansu, 730030, China

    Qingjiang Xiao, Shiqiang Du, Baokai Liu & Jinmei Song

  2. College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, 730030, China

    Shiqiang Du & Yao Yu

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  1. Qingjiang Xiao
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Correspondence to Shiqiang Du.

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Xiao, Q., Du, S., Liu, B. et al. Multi-view spectral clustering based on adaptive neighbor learning and low-rank tensor decomposition. Multimed Tools Appl 82, 41159–41186 (2023). https://doi.org/10.1007/s11042-023-15018-4

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