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Fast and General Incomplete Multi-view Adaptive Clustering

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Abstract

With the development of data collection technologies, multi-view clustering (MVC) has become an emerging research topic. The traditional MVC method cannot process incomplete views. In recent years, although many incomplete multi-view clustering methods have been proposed by many researchers, these methods still suffer from some limitations. For example, these methods all have parameters that need to be adjusted, or have high computational complexity and are not suitable for processing large-scale data. To make matters worse, these methods are not suitable for cases where there are no paired samples among multiple views. The above limitations make existing methods difficult to apply in practice. This paper proposes a Fast and General Incomplete Multi-view Adaptive Clustering (FGPMAC) method. The FGPMAC adopts an adaptive neighbor assignment strategy to independently construct the similarity matrix of each view, thereby it can handle the cases where there are no paired samples among multiple views, and eliminating the necessary to adjust the parameters. Moreover, by adopting a non-iterative approach, FGPMAC has low computational complexity and is suitable for large-scale datasets. Results of experiments on multiple real datasets fully demonstrate the advantages of FGPMAC, such as simplicity, effectiveness and superiority.

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Data Availability

The data and code that support the findings of this study are openly available at https://github.com/leiyang617/code_for_FGIMAC.

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Funding

This work was supported in part by the Natural Science Foundation of China under Grant 61972001, in part by the General Project of Anhui Natural Science Foundation under Grant 1908085MF188 and 2108085MF212, and in part by the Key Projects of Natural Science Foundation of Anhui Province Colleges and Universities under Grant KJ2020A0041.

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Xia Ji, Lei Yang, Sheng Yao, Peng Zhao & Xuejun Li

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  1. Xia Ji
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  2. Lei Yang
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  3. Sheng Yao
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  4. Peng Zhao
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  5. Xuejun Li
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Correspondence to Lei Yang.

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Ji, X., Yang, L., Yao, S. et al. Fast and General Incomplete Multi-view Adaptive Clustering. Cogn Comput 15, 683–693 (2023). https://doi.org/10.1007/s12559-022-10079-3

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