An Information-Theoretic Account of Static Index Pruning

In this paper, we recast static index pruning as a model induction problem under the framework of Kullback's principle of minimum cross-entropy. We show that static index pruning has an approximate analytical solution in the form of convex integer program. Further analysis on computation feasibility suggests that one of its surrogate model can be solved efficiently. This result has led to the rediscovery of uniform pruning, a simple yet powerful pruning method proposed in 2001 and later easily ignored by many of us. To empirically verify this result, we conducted experiments under a new design in which prune ratio is strictly controlled. Our result on standard ad-hoc retrieval benchmarks has confirmed that uniform pruning is robust to high prune ratio and its performance is currently state of the art.