Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Susanne Still, William Bialek, Léon Bottou
We argue that K–means and deterministic annealing algorithms for geo- metric clustering can be derived from the more general Information Bot- tleneck approach. If we cluster the identities of data points to preserve information about their location, the set of optimal solutions is massively degenerate. But if we treat the equations that define the optimal solution as an iterative algorithm, then a set of “smooth” initial conditions selects solutions with the desired geometrical properties. In addition to concep- tual unification, we argue that this approach can be more efficient and robust than classic algorithms.