• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >Model-based clustering with sparse covariance matrices
【24h】

Model-based clustering with sparse covariance matrices

机译：基于模型的群体与稀疏协方差矩阵

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Finite Gaussian mixture models are widely used for model-based clustering of continuous data. Nevertheless, since the number of model parameters scales quadratically with the number of variables, these models can be easily over-parameterized. For this reason, parsimonious models have been developed via covariance matrix decompositions or assuming local independence. However, these remedies do not allow for direct estimation of sparse covariance matrices nor do they take into account that the structure of association among the variables can vary from one cluster to the other. To this end, we introduce mixtures of Gaussian covariance graph models for model-based clustering with sparse covariance matrices. A penalized likelihood approach is employed for estimation and a general penalty term on the graph configurations can be used to induce different levels of sparsity and incorporate prior knowledge. Model estimation is carried out using a structural-EM algorithm for parameters and graph structure estimation, where two alternative strategies based on a genetic algorithm and an efficient stepwise search are proposed for inference. With this approach, sparse component covariance matrices are directly obtained. The framework results in a parsimonious model-based clustering of the data via a flexible model for the within-group joint distribution of the variables. Extensive simulated data experiments and application to illustrative datasets show that the method attains good classification performance and model quality. The general methodology for model-based clustering with sparse covariance matrices is implemented in the R package mixggm, available on CRAN.
机译：有限高斯混合模型广泛用于基于模型的连续数据集群。然而，由于模型参数的数量与变量的数量逐步缩放，因此这些模型可以很容易地参数化。因此，已经通过协方差矩阵分解或假设局部独立性制定了解析模型。但是，这些补救措施不允许直接估计稀疏协方差矩阵，也不考虑到变量之间的关联结构可以从一个群集中变化。为此，我们介绍了具有稀疏协方差矩阵的基于模型的聚类的高斯协方差图模型的混合物。采用惩罚的似然方法来估计，并且图表配置上的一般惩罚术语可用于诱导不同水平的稀疏性并纳入先前的知识。使用用于参数和图形结构估计的结构-EM算法进行模型估计，其中提出了基于遗传算法和有效逐步搜索的两个替代策略进行推理。利用这种方法，直接获得稀疏分量协方差矩阵。该框架通过灵活的模型导致数据的基于模型的基于模型的基于模型的集群，用于内部的变量内的组联接分布。广泛的模拟数据实验和应用于说明性数据集的应用表明，该方法达到了良好的分类性能和模型质量。基于模型的群集与稀疏协方差矩阵的一般方法是在R包MixGGM中实现的，在CRAN上可用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号