Abstract
Most of multiple kernel clustering algorithms aim to find the optimal kernel combination and have to calculate kernel weights iteratively. For the kernel methods, the scale parameter of Gaussian kernel is usually searched in a number of candidate values of the parameter and the best is selected. In this paper, a novel locally adaptive multiple kernel k-means algorithm is proposed based on shared nearest neighbors. Our similarity measure meets the requirements of the clustering hypothesis, which can describe the relations between data points more reasonably by taking local and global structures into consideration. We assign to each data point a local scale parameter and combine the parameter with shared nearest neighbors to construct kernel matrix. According to the local distribution, the local scale parameter of Gaussian kernel is generated adaptively. Experiments show that the proposed algorithm can effectively deal with the clustering problem of data sets with complex structure or multiple scales.
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Acknowledgements
This work was supported by the ‘Fundamental Research Funds for the Central Universities’ (No. 2017XKQY076).
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Shifei Ding declares that he has no conflict of interest. Shuyan Fan declares that she has no conflict of interest. Yu Xue declares that he has no conflict of interest.
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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.
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Ding, S., Xu, X., Fan, S. et al. Locally adaptive multiple kernel k-means algorithm based on shared nearest neighbors. Soft Comput 22, 4573–4583 (2018). https://doi.org/10.1007/s00500-017-2640-5
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DOI: https://doi.org/10.1007/s00500-017-2640-5