Composite kernels for semi-supervised clustering
Composite kernels for semi-supervised clustering
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DOI：
10.1007/s10115-010-0318-8
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发表时间：
2011-07
影响因子：
2.7
通讯作者：
C. Domeniconi;Jing Peng;B. Yan
中科院分区：
文献类型：
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作者：
C. Domeniconi;Jing Peng;B. Yan
基于核方法的一个关键问题是如何选择最优的核。核函数必须符合学习目标，以获得有意义的结果。虽然已经在监督设置中提出了估计最优核函数和相应参数的问题的解决方案，但是当没有标记数据可用时仍然是一个挑战，并且我们所拥有的只是一组成对的must-linkandcannot-linkconstraints。在本文中，我们解决的问题，优化核函数使用成对约束的半监督聚类。我们提出了一种新的优化标准，用于直接根据数据和给定的约束自动估计复合高斯核的最佳参数。我们联合收割机我们的建议与半监督基于内核的算法，实验证明我们的方法的有效性。实验结果表明，该方法对于基于核的半监督聚类是非常有效的。
A critical problem related to kernel-based methods is how to selectoptimalkernels. A kernel function must conform to the learning target in order to obtain meaningful results. While solutions to the problem of estimating optimal kernel functions and corresponding parameters have been proposed in a supervised setting, it remains a challenge when no labeled data are available, and all we have is a set of pairwisemust-linkandcannot-linkconstraints. In this paper, we address the problem of optimizing the kernel function using pairwise constraints for semi-supervised clustering. We propose a new optimization criterion for automatically estimating the optimal parameters of composite Gaussian kernels, directly from the data and given constraints. We combine our proposal with a semi-supervised kernel-based algorithm to demonstrate experimentally the effectiveness of our approach. The results show that our method is very effective for kernel-based semi-supervised clustering.