Use of prior knowledge in a non-Gaussian method for learning linear structural equation models

Authors:
Takanori Inazumi;Shohei Shimizu;Takashi Washio
Affiliations:
The Institute of Scientific and Industrial Research, Osaka University, Japan;The Institute of Scientific and Industrial Research, Osaka University, Japan;The Institute of Scientific and Industrial Research, Osaka University, Japan
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

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.