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2019-10-22,张莉莉博士,Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

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报告题目:Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

  人: 张莉莉博士

摘要:

Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering. However, this approach is sensitive to both diverse non-normalities of marginal variables and outliers, which are commonly observed in multivariate experiments. We propose mixtures of Gaussian copula factor analyzers (MGCFA) for clustering high-dimensional data.

This model has two advantages; 1) it allows dierent marginal distributions to facilitate fitting exibility of the mixture model, 2) it can avoid the curse of dimensionality by embedding the factor-analytic structure in the component-correlation matrices of the mixture distribution.

 间:20191022日(周下午) 1000—11:30

 点:逸夫楼1417

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