Similarity kernels via bi-clustering for conventional intergovernmental organizations

Many databases provide tabular data relating objects to entities; for example, which countries belong to certain organizations. We seek to infer implicit organizational variables over such objects (countries) as a function of these properties (organizational memberships), and vice versa. If kernels existed over objects, then machine learning and non-linear dimensionality reduction techniques could be used. But this requires a similarity or distance defined over objects, which does not exist a priori. We are exploring an approach to kernel identification based on bi-clustering in which an average over randomized biclusters approximates a kernel. We claim that such kernels provide a viable alternative to other, more common kernel approaches. Experiments with a database of memberships in conventional intergovernmental organizations supports this claim.