It really seems like this ought to work. I have no idea what I'm talking about and want you to tell me if it works or not. 1. support vector trees. This might involve some restriction on the SVM mappings we can use, e.g., we might have to limit the SVM to affine mappings to make window queries work. 2. wavelet trees.
Wavelets provide another way to construct a multiresolution decomposition.
3. space-filling curves. WAL main-memory databases. Read the papers carefully. Measure and evaluate. The Vapnik-Chervonenkis dimension is a concept from machine learning that ties the storage requirements of a learning technique to its ability to the quality of its answers to classification problems.