Estimation of linear non-Gaussian acyclic models for latent factors
Estimation of linear non-Gaussian acyclic models for latent factors
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DOI：
10.1016/j.neucom.2008.11.018
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发表时间：
2009-03
期刊：
影响因子：
6
通讯作者：
Shohei Shimizu;P. Hoyer;Aapo Hyvärinen
中科院分区：
文献类型：
--
作者：
Shohei Shimizu;P. Hoyer;Aapo Hyvärinen
已经提出了许多方法来发现观测变量之间的因果关系。但人们往往希望发现潜在因素之间的因果关系，而不是观察到的变量。已经提出了一些方法来估计由观测变量测量的潜在因素的线性非循环模型。然而，大多数方法仅使用数据协方差结构进行模型识别，这导致了许多不可区分的模型。在本文中，我们表明，一个线性非循环模型的潜在因素是可识别的数据时，非高斯。
Many methods have been proposed for discovery of causal relations among observed variables. But one often wants to discover causal relations among latent factors rather than observed variables. Some methods have been proposed to estimate linear acyclic models for latent factors that are measured by observed variables. However, most of the methods use data covariance structure alone for model identification, and this leads to a number of indistinguishable models. In this paper, we show that a linear acyclic model for latent factors is identifiable when the data are non-Gaussian.