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Adaptive sparse graph learning for multi-view spectral clustering

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Abstract

In recent years, graph-based multi-view spectral clustering methods have achieved remarkable progress. However, most of which are generally deficient in the following two aspects. First, ignoring the different importance of multiple views, low-quality views in the multi-view data may seriously affect the clustering performance. Second, for constructed graphs, noise and outliers are difficult to effectively filter out. For the purpose of overcoming the above two deficiencies, this paper proposes a novel adaptive sparse graph learning for multi-view spectral clustering (ASGL) method. Specifically, the adaptive neighbor graph learning method is adopted to construct the similarity matrices of all views, which improves the robustness to noise and outliers. By adaptively assigning the weight of each view, the complementary information between the views is combined to more accurately describe the essential category attributes between the sample data. An effective algorithm for solving the optimization problem of ASGL model is proposed. Compared to several state-of-the-art algorithms, extensive experiments on several benchmark datasets verify good clustering performance of ASGL.

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Data Availability

Data openly available in a public repository.

Notes

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

  2. http://mlg.ucd.ie/datasets/3sources.html

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61866033), the Introduction of Talent Research Project of Northwest Minzu University (No. xbmuyjrc201904), the Fundamental Research Funds for the Central Universities of Northwest Minzu University (No.31920220019, 31920220130), the Gansu Provincial First-class Discipline Program of Northwest Minzu University (No.11080305), the Leading Talent of National Ethnic Affairs Commission (NEAC), the Young Talent of NEAC, and the Innovative Research Team of NEAC (2018) 98.

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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, Kaiwu Zhang & Jinmei Song

  2. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China

    Qingjiang Xiao

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

    Shiqiang Du & Yixuan Huang

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  1. Qingjiang Xiao
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Correspondence to Shiqiang Du.

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Xiao, Q., Du, S., Zhang, K. et al. Adaptive sparse graph learning for multi-view spectral clustering. Appl Intell 53, 14855–14875 (2023). https://doi.org/10.1007/s10489-022-04267-9

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