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Pairwise Clustering with Matrix Factorisation and the EM Algorithm

机译:与矩阵分子和EM算法的成对聚类

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In this paper we provide a direct link between the EM algorithm and matrix factorisation methods for grouping via pairwise clustering. We commence by placing the pairwise clustering process in the setting of the EM algorithm. We represent the clustering process using two sets of variables which need to be estimated. The first of these are cluster-membership indicators. The second are revised link-weights between pairs of nodes. We work with a model of the grouping process in which both sets of variables are drawn from a Bernoulli distribution. The main contribution in this paper is to show how the cluster-memberships may be estimated using the leading eigenvector of the revised link-weight matrices. We also establish convergence conditions for the resulting pairwise clustering process. The method is demonstrated on the problem of multiple moving object segmentation.
机译:在本文中,我们提供了通过成对聚类进行分组的EM算法和矩阵分子方法之间的直接链路。我们开始在EM算法的设置中放置成对聚类过程。我们使用需要估计的两组变量来表示聚类过程。其中的第一个是集群成员资格指标。第二个是在节点对之间的修订链路权重。我们使用分组过程的模型,其中来自Bernoulli分发的两组变量。本文的主要贡献是展示如何使用修改后的链路权重矩阵的领先特征向量估计群集成员资格。我们还建立了由此产生的成对聚类过程的收敛条件。对多重移动对象分割的问题进行了说明该方法。

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