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摘要: Kernel Grower 是一种有效的核聚类方法, 它具有计算精度高的优点. 然而, Kernel Grower在应用中的一个关键问题是对于大规模数据运算速度缓慢, 这在很大程度上制约了该方法的应用. 本文提出了一种大规模数据的快速核聚类方法, 该方法通过近似最小包含球快速算法, 显著地提高了的Kernel Grower计算速度, 并且该方法的计算复杂度仅与样本个数成线性关系. 在人工数据集和标准测试集上的模拟实验均说明本文算法的有效性. 本文还给出该方法在真实彩色图像分割中应用.Abstract: Kernel grower is a novel kernel clustering method proposed recently by Camastra and Verri. It shows good performance for various data sets and compares favorably with respect to popular clustering algorithms. However, the main drawback of the method is the weak scaling ability in dealing with large data sets, which restricts its application greatly. In this paper, we propose a scaled-up kernel grower method using core-sets, which is significantly faster than the original method for large data clustering. Meanwhile, it can deal with very large data sets. Numerical experiments on benchmark data sets as well as synthetic data sets show the efficiency of the proposed method. The method is also applied to real image segmentation to illustrate its performance.
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Key words:
- Kernel clustering /
- core-set /
- large data sets /
- image segmentation /
- pattern recognition
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