L1 正則化法に基づくグラフィカルガウシアンモデリングについて

抄録

Graphical Gaussian models have received considerable attention in various field of research such as bioinformatics. We propose a new method on the parameter estimation and model selection in graphical Gaussian models based on L1-regularization. In the proposed method, the structural learning for graphical Gaussian models is equivalent to the selection of the regularization parameters in the L1-regularization. We investigate this problem from a Bayes approach and derive an empirical Bayesian information criterion for choosing them. We analyze Arabidopsis thaliana microarray data and estimate gene networks.