Abstract
A new histogram-based mutual information estimator using data-driven tree-structured partitions (TSP) is presented in this paper. The derived TSP is a solution to a complexity regularized empirical information maximization, with the objective of finding a good tradeoff between the known estimation and approximation errors. A distribution-free concentration in equality for this tree-structured learning problem as well as finite sample performance bounds for the proposed histogram-based solution is derived. It is shown that this solution is density-free strongly consistent and that it provides, with an arbitrary high probability, an optimal balance between the mentioned estimation and approximation errors. Finally, for the emblematic scenario of independence, I(X; Y) = 0, it is shown that the TSP estimate converges to zero with o(e-n1/3+ log log n).
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