论文标题
$ k $ -Means和Gaussian混合物建模与分离约束
$K$-Means and Gaussian Mixture Modeling with a Separation Constraint
论文作者
论文摘要
我们考虑使用$ k $ -MEANS和GAUSSIAN混合模型聚类的问题，并在现实价值数据的背景下对中心之间的分离有限制。我们首先提出了一种动态的编程方法，用于解决$ k $ - 均值问题，并在中心上有分离约束（Wang and Song，2011年）。在拟合高斯混合模型的背景下，我们提出了一种包含这种约束的EM算法。分离约束可以帮助将聚类算法的输出正规化，我们提供模拟和真实的数据示例以说明这一点。
We consider the problem of clustering with $K$-means and Gaussian mixture models with a constraint on the separation between the centers in the context of real-valued data. We first propose a dynamic programming approach to solving the $K$-means problem with a separation constraint on the centers, building on (Wang and Song, 2011). In the context of fitting a Gaussian mixture model, we then propose an EM algorithm that incorporates such a constraint. A separation constraint can help regularize the output of a clustering algorithm, and we provide both simulated and real data examples to illustrate this point.
