Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models
Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models
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DOI：
10.1007/978-3-642-15995-4_28
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发表时间：
2010-09
期刊：
影响因子：
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通讯作者：
Takanori Inazumi;Shohei Shimizu;T. Washio
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文献类型：
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作者：
Takanori Inazumi;Shohei Shimizu;T. Washio
我们讨论基于线性结构方程模型的因果结构学习。传统的学习方法通常假设高斯性并创建许多不可区分的模型。因此，在许多情况下，很难获得有关结构的大量信息。最近，一种称为LiNGAM的非高斯学习方法被提出来识别模型结构，而不使用关于结构的先验知识。然而，如果结构的一部分的一些先验知识是可用的，则可以实现更有效的学习。在本文中，我们提出了使用先验知识，以提高性能的最先进的非高斯方法。在人工数据上的实验表明，即使先验知识量不是很大，该方法的准确性和计算时间也得到了显著提高。
We discuss causal structure learning based on linear structural equation models. Conventional learning methods most often assume Gaussianity and create many indistinguishable models. Therefore, in many cases it is difficult to obtain much information on the structure. Recently, a non-Gaussian learning method called LiNGAM has been proposed to identify the model structure without using prior knowledge on the structure. However, more efficient learning can be achieved if some prior knowledge on a part of the structure is available. In this paper, we propose to use prior knowledge to improve the performance of a state-of-art non-Gaussian method. Experiments on artificial data show that the accuracy and computational time are significantly improved even if the amount of prior knowledge is not so large.