Estimating the inverse trace using random forests on graphs

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Using Machine learning techniques to perform machine learning computation, I really like the meta aspect of this paper. Estimating the inverse trace using random forests on graphs by Simon Barthelmé, Nicolas Tremblay, Alexandre Gaudillière, Luca Avena, Pierre-Olivier Amblard

Some data analysis problems require the computation of (regularised) inverse traces, i.e. quantities of the form $\Tr (q \bI + \bL)^{-1}$. For large matrices, direct methods are unfeasible and one must resort to approximations, for example using a conjugate gradient solver combined with Girard's trace estimator (also known as Hutchinson's trace estimator). Here we describe an unbiased estimator of the regularized inverse trace, based on Wilson's algorithm, an algorithm that was initially designed to draw uniform spanning trees in graphs. Our method is fast, easy to implement, and scales to very large matrices. Its main drawback is that it is limited to diagonally dominant matrices $\bL$.

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