From the post:
(tl;dr: Our upcoming paper in SoCG 2012 shows that with a nontrivial amount of work, you can do approximate Bregman near neighbor queries in low dimensional spaces in logarithmic query time)
Or you may prefer the full paper:
In this paper we present the first provable approximate nearest-neighbor (ANN) algorithms for Bregman divergences. Our first algorithm processes queries in O(log^d n) time using O(n log^d n) space and only uses general properties of the underlying distance function (which includes Bregman divergences as a special case). The second algorithm processes queries in O(log n) time using O(n) space and exploits structural constants associated specifically with Bregman divergences. An interesting feature of our algorithms is that they extend the ring-tree + quad-tree paradigm for ANN searching beyond Euclidean distances and metrics of bounded doubling dimension to distances that might not even be symmetric or satisfy a triangle inequality.
Tough sledding but interesting work on Bregman divergences. Leads to proposed improvements in data search structures.
The following references may be helpful: Bregman divergence