中国矿业大学(北京)tyc1286太阳成集团
学术报告
报告题目:Mixtures of Gaussian copula factor analyzers for clustering high dimensional data
报 告 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.
时 间:2019年10月22日(周二下午) 10:00—11:30
地 点:逸夫楼1417
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