Variational learning of clusters of undercomplete nonsymmetric independent components
Variational learning of clusters of undercomplete nonsymmetric independent components
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DOI：
10.1162/153244303768966120
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发表时间：
2003-01-01
影响因子：
6
通讯作者：
Sejnowski, TJ
中科院分区：
文献类型：
--
作者：
Chan, K;Lee, TW;Sejnowski, TJ
我们应用变分方法来自动确定高维数据集中独立分量的混合数，其中源可能是非对称分布的。数据通过聚类进行建模，其中每个聚类被描述为独立因素的线性混合。变分贝叶斯方法为观察到的数据产生准确的密度模型，而不会出现过拟合问题。这允许为每个聚类识别数据的维度。新方法已成功应用于诊断青光眼的困难现实医学数据集。
We apply a variational method to automatically determine the number of mixtures of independent components in high-dimensional datasets, in which the sources may be nonsymmetrically distributed. The data are modeled by clusters where each cluster is described as a linear mixture of independent factors. The variational Bayesian method yields an accurate density model for the observed data without overfitting problems. This allows the dimensionality of the data to be identified for each cluster. The new method was successfully applied to a difficult real-world medical dataset for diagnosing glaucoma.