Abstract
High-dimensional data poses a great challenge to clustering, and subspace clustering algorithms have unique advantages when working with high-dimensional data. However, there are still some difficulties in adapting the nonlinear feature dimension, the selection and collaborative settings of kernel functions. Therefore, in this study, we come up with a feature reduction-induced subspace multiple kernel fuzzy clustering. Firstly, in order to solve the information loss caused by the nonlinear characteristics of the data, the multiple kernel clustering method is introduced, and some nonlinear features of the data are collaboratively mapped to the high-dimensional linear space, so as to better understand the characteristic information of the data. Secondly, due to the complexity of subspace and multiple kernel learning, we introduce the idea of feature reduction, and reduce the data dimension according to the importance of information, which can reduce the complexity of the algorithm on the one hand, and improve the role of important feature attributes in clustering on the other hand. Finally, the proposed RS-MKFC algorithm and the related 6 algorithms are compared on 6 ordinary datasets and 4 high-dimensional datasets, and it is found that the proposed algorithms are superior over the other 6 algorithms. At the same time, we discover the ability of RS-MKFC algorithm to screen important features as well as the function of feature reduction, and good results are achieved.
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Acknowledgment
This work has been supported by the National Natural Science Foundation of China (Nos. 62176083, 62176084, 61877016, and 61976078), the Key Research and Development Program of Anhui Province (No. 202004d07020004), the Natural Science Foundation of Anhui Province (No. 2108085MF203), and the Fundamental Research Funds for Central Universities of China (No. PA2021GDSK0092).
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Tang, Y., Li, B., Pan, Z., Sun, X., Chen, R. (2023). A Feature Reduction-Induced Subspace Multiple Kernel Fuzzy Clustering Algorithm. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1682. Springer, Singapore. https://doi.org/10.1007/978-981-99-2385-4_20
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DOI: https://doi.org/10.1007/978-981-99-2385-4_20
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