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An Efficient Algorithm Combining Spectral Clustering with Feature Selection

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Abstract

Traditional clustering algorithms have some limitations, which are sensitive to noise and mostly applicable to convex data sets. To solve these problems, the paper proposes a novel algorithm combining spectral clustering with feature selection. Specifically, the loss item is marked with a root that can reduce the deviation value then improve the robustness of the model. And in the algorithm optimization, there is one parameter is represented by other known parameters, which can reduce the time of parameter adjustment. Then, the regular term \({{\ell }_{2,p}}\text {-norm}\) is applied to reduce the influence of noise and redundant features and prevent the model from overfitting. Finally, Laplace matrix is constructed by kNN algorithm which is used to learn subspace and to preserve the local structure among samples, and the data after dimension reduction is used to spectral clustering. Experimental analysis on 10 benchmark datasets show that the proposed algorithm is more outperformed than the algorithms of the state-of-the-art.

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Acknowledgements

This work is partially supported by the China Key Research Program (Grant No. 2016YFB1000905); the Key Program of the National Natural Science Foundation of China (Grant No. 61836016); the Natural Science Foundation of China (Grants Nos. 61876046, 61573270, 81701780, 61672177 and 61972177); the Project of Guangxi Science and Technology (GuiKeAD17195062); the Guangxi Natural Science Foundation (Grant No. 2017GXNSFBA198221); the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; the Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents; the Research Fund of Guangxi Key Lab of Multisource Information Mining & Security (18-A-01-01); and 2019 basic scientific research capability enhancement project for middle-aged teachers in guangxi university (2019KY0062); Innovation Project of Guangxi Graduate Education (YCBZ2020038, JGY2020026).

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi, China

    Qimin Luo, Guoqiu Wen, Leyuan Zhang & Mengmeng Zhan

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  1. Qimin Luo
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  2. Guoqiu Wen
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  3. Leyuan Zhang
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  4. Mengmeng Zhan
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Correspondence to Guoqiu Wen.

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Luo, Q., Wen, G., Zhang, L. et al. An Efficient Algorithm Combining Spectral Clustering with Feature Selection. Neural Process Lett 52, 1913–1925 (2020). https://doi.org/10.1007/s11063-020-10297-6

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