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Joint Robust Multi-view Spectral Clustering

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Abstract

Current multi-view clustering algorithms use multistage strategies to conduct clustering, or require cluster number or similarity matrix prior, or suffer influence of irrelevant features and outliers. In this paper, we propose a Joint Robust Multi-view (JRM) spectral clustering algorithm that considers information from all views of the multi-view dataset to conduct clustering and solves the issues, such as initialization, cluster number determination, similarity measure, feature selection, and outlier reduction around clustering, in a unified way. The optimal performance could be reached when all views are considered and the separated stages are combined in a unified way. Experiments have been performed on six real-world benchmark datasets and our proposed JRM algorithm outperforms the comparison clustering algorithms in terms of two evaluation metrics for clustering algorithms including accuracy and purity.

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Authors and Affiliations

  1. School of Natural and Computational Sciences, Massey University, Auckland, New Zealand

    Tong Liu

  2. New Zealand Institute for Advanced Study, Auckland, New Zealand

    Gaven Martin

  3. Hebei GEO University, ShiJiaZhuang, China

    YongXin Zhu

  4. College of Big Data, Yunnan Agricultural University, Kunming, China

    Lin Peng

  5. College of Information and Electrical Engineering, China Agricultural University, Beijing, China

    Li Li

Authors
  1. Tong Liu
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  2. Gaven Martin
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  3. YongXin Zhu
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  4. Lin Peng
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  5. Li Li
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Correspondence to Tong Liu.

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Liu, T., Martin, G., Zhu, Y. et al. Joint Robust Multi-view Spectral Clustering. Neural Process Lett 52, 1843–1862 (2020). https://doi.org/10.1007/s11063-020-10257-0

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