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Random Feature Map-Based Multiple Kernel Fuzzy Clustering with All Feature Weights

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Abstract

Kernel clustering methods are useful to discover the non-linear structures hidden in data, but they suffer from the difficulty of kernel selection and high computational complexity. In this paper, we propose a novel random feature map-based multiple kernel fuzzy clustering method with all feature weights, in which low-rank randomized features of multiple kernels are generated by random Fourier feature map and Quasi-Monte Carlo feature map, and maximum entropy technique is applied to optimize the weights of all feature attributes. The proposed method is effective to extract important kernel and the important attributes of the kernel so as to achieve good clustering results. What is more, compared with conventional kernel clustering methods, our method is much more time-saving and is available to large data sets. The experiments based on various data sets show the superiority and efficiency of the proposed method.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants with Nos. 61873324 and 61573166, the Natural Science Foundation of Shandong Province under Grant with Nos. ZR2019MF040 and ZR2017MF044, the Shandong Province Key Research and Development Program under Grant with Nos. 2018GGX101048 and 2018GGX101016, and the Project of Shandong Province Higher Educational Science and Technology Program under Grant with No. J16LN07.

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, 250022, China

    Yingxu Wang, Jiwen Dong, Jin Zhou, Guangmei Xu & Yuehui Chen

  2. Department of Computer and Information Science, University of Macau, Macau, 999078, China

    Yingxu Wang

Authors
  1. Yingxu Wang
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  2. Jiwen Dong
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  3. Jin Zhou
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  4. Guangmei Xu
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  5. Yuehui Chen
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Correspondence to Jin Zhou.

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Wang, Y., Dong, J., Zhou, J. et al. Random Feature Map-Based Multiple Kernel Fuzzy Clustering with All Feature Weights. Int. J. Fuzzy Syst. 21, 2132–2146 (2019). https://doi.org/10.1007/s40815-019-00713-y

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  • DOI: https://doi.org/10.1007/s40815-019-00713-y

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