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Multi-view Clustering Based on Low-rank Representation and Adaptive Graph Learning

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Abstract

The multi-view algorithm based on graph learning pays attention to the manifold structure of data and shows good performance in clustering task. However, multi-view data usually contains noise, which reduces the robustness of the multi-view clustering algorithm. Besides, any single local information cannot adequately express the whole frame perfectly. Graph learning method often ignores the global structure of data, resulting in suboptimal clustering effect. In order to address the above problems, we propose a novel multi-view clustering model, namely multi-view clustering based on low-rank representation and adaptive graph learning (LRAGL). The noise and outliers in the original data are considered when constructing the graph and the adaptive learning graphs are employed to describe the relationship between samples. Specifically, LRAGL enjoys the following advantages: (1) The graph constructed on the low-rank representation coefficients after filtering out the noise can more accurately reveal the relationship between the samples; (2) Both the global structure (low-rank constraints) and the local structure (adaptive neighbors learning) in the multi-view data are captured; (3) The filtering of noise and the construction of the similarity graph of each view data are integrated into a framework to obtain the overall optimal solution; LRAGL model can be optimized efficiently by utilizing the augmented Lagrangian multiplier with Alternating Direction Minimization Method (ADMM). Extensive experimental results on six benchmark datasets verify the superiority of the proposed method in clustering.

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Notes

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

  2. 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 Introduction of Talent Research Project of Northwest Minzu University (No. xbmuyjrc201904), the Fundamental Research Funds for the Central Universities of Northwest Minzu University (Nos. 31920200064, 31920200097), 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. College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu, 730030, China

    Yixuan Huang, Shiqiang Du & Yao Yu

  2. 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

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  1. Yixuan Huang
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Correspondence to Shiqiang Du.

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Huang, Y., Xiao, Q., Du, S. et al. Multi-view Clustering Based on Low-rank Representation and Adaptive Graph Learning. Neural Process Lett 54, 265–283 (2022). https://doi.org/10.1007/s11063-021-10634-3

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