Information preservation in static index pruning
We develop a new static index pruning criterion based on the notion of information preservation. This idea is motivated by the fact that model degeneration, as does static index pruning, inevitably reduces the predictive power of the resulting model. We model this loss in predictive power using conditional entropy and show that the decision in static index pruning can therefore be optimized to preserve information as much as possible. We evaluated the proposed approach on three different test corpora, and the result shows that our approach is comparable in retrieval performance to state-of-the-art methods. When efficiency is of concern, our method has some advantages over the reference methods and is therefore suggested in Web retrieval settings.