  In this work, we introduce a new procedure for applying Restricted Boltzmann
Machines (RBMs) to missing data inference tasks, based on linearization of the
effective energy function governing the distribution of observations. We
compare the performance of our proposed procedure with those obtained using
existing reconstruction procedures trained on incomplete data. We place these
performance comparisons within the context of the perception-distortion
trade-off observed in other data reconstruction tasks, which has, until now,
remained unexplored in tasks relying on incomplete training data.
