  Dependence strucuture estimation is one of the important problems in machine
learning domain and has many applications in different scientific areas. In
this paper, a theoretical framework for such estimation based on copula and
copula entropy -- the probabilistic theory of representation and measurement of
statistical dependence, is proposed. Graphical models are considered as a
special case of the copula framework. A method of the framework for estimating
maximum spanning copula is proposed. Due to copula, the method is irrelevant to
the properties of individual variables, insensitive to outlier and able to deal
with non-Gaussianity. Experiments on both simulated data and real dataset
demonstrated the effectiveness of the proposed method.

Machine Learning;Information Retrieval;Methodology