非参数分层聚类模型

S. Mohamad, A. Bouchachia, M. S. Mouchaweh
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引用次数: 4

摘要

提出了一种基于高斯混合模型(NHCM)的非参数聚类模型。NHCM使用一种新颖的Dirichlet过程(DP)先验,允许对数据进行更灵活的建模,其中DP的基本分布本身就是高斯共轭先验的无限混合。NHCM可以看作是一种分层聚类模型,低水平的基础先验控制构成子簇的数据点的分布,高水平的先验控制构成簇的子簇的分布。使用这种分层配置,我们可以保持模型的低复杂性,并允许对倾斜的复杂数据进行聚类。为了进行推理,我们提出了一种Gibbs抽样算法。实证研究分析了所提出的聚类模型的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A non-parametric hierarchical clustering model
We present a novel non-parametric clustering model using Gaussian mixture model (NHCM). NHCM uses a novel Dirichlet process (DP) prior allowing for more flexible modeling of the data, where the base distribution of DP is itself an infinite mixture of Gaussian conjugate prior. NHCM can be thought of as hierarchical clustering model, in which the low level base prior governs the distribution of the data points forming sub-clusters, and the higher level prior governs the distribution of the sub-clusters forming clusters. Using this hierarchical configuration, we can maintain low complexity of the model and allow for clustering skewed complex data. To perform inference, we propose a Gibbs sampling algorithm. Empirical investigations have been carried out to analyse the efficiency of the proposed clustering model.
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