A Local Learning Approach for Clustering
A Local Learning Approach for Clustering
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DOI：
10.7551/mitpress/7503.003.0196
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发表时间：
2006-12
影响因子：
19
通讯作者：
Mingrui Wu;B. Scholkopf
中科院分区：
文献类型：
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作者：
Mingrui Wu;B. Scholkopf
我们提出了一个局部学习的聚类方法。其基本思想是，一个好的聚类结果应该具有的属性，每个数据点的聚类标签可以很好地预测其相邻数据和它们的聚类标签的基础上，使用当前的监督学习方法。一个优化问题被公式化，使得它的解具有上述性质。采用松弛法和特征分解法求解该优化问题。我们还简要地探讨了参数选择问题，并提供了一个简单的参数选择方法所提出的算法。实验结果验证了该方法的有效性。
We present a local learning approach for clustering. The basic idea is that a good clustering result should have the property that the cluster label of each data point can be well predicted based on its neighboring data and their cluster labels, using current supervised learning methods. An optimization problem is formulated such that its solution has the above property. Relaxation and eigen-decomposition are applied to solve this optimization problem. We also briefly investigate the parameter selection issue and provide a simple parameter selection method for the proposed algorithm. Experimental results are provided to validate the effectiveness of the proposed approach.