Recovering Information from Summary Data

We formulate this task as an inverse problem, specifying a well-defined cost function that has to be optimized under constraints. We show that our formulation includes the uniformity and independence assumptions as a special case, and that it can achieve better reconstruction results if we maximize the smoothness as opposed to the uniformity. In our experiments on real and synthetic datasets, the proposed method almost consistently outperforms its competitor, improving the rootmean-square error by up to 20 per cent for stock price data, and up to 90 per cent for smoother data sets.